CMIP6 prep
Camille
2024-02-19
library(raster)
library(plyr)
library(dplyr)
library(ggplot2)
library(viridisLite)
library(colorspace)
library(RColorBrewer)
library(sf)
library(stringr)
library(ggpubr)
library(kableExtra)
memory.limit(size = 160000)
## [1] Inf
#display.brewer.pal(n = 11, name ="RdYlGn")
colorvec <- brewer.pal(n = 11, name ="RdYlGn")1 Data
land_mask <- raster("Masks/land_sea_mask_1degree.nc4")
land_mask.df <- as.data.frame(land_mask, xy = T) %>% setNames(c("lon","lat","lm")) # 0 if ocean, 1 if land
elev <- raster("Worldclim/wc2.1_10m/wc2.1_10m_elev.tif")
elev.df <- elev %>% projectRaster(to = land_mask) %>% as.data.frame(xy = T) %>% setNames(c("lon","lat", "z"))
ipcc_regions <- shapefile("Masks/IPCC-WGI-reference-regions-v4.shp") %>% spTransform(crs("EPSG:4326"))
ipcc_regions.raster <- ipcc_regions %>% rasterize(land_mask)
ipcc_regions.df <- as.data.frame(ipcc_regions.raster, xy = T) %>% setNames(c("lon","lat","Continent","Type","Name","Acronym"))tas.all_annual <- read.table("CMIP6/tas/tas.all_annual.txt")
pr.all_annual <- read.table("CMIP6/pr/pr.all_annual.txt")
sfcWind.all_annual <- read.table("CMIP6/sfcWind/sfcWind.all_annual.txt")
hfls.all_annual <- read.table("CMIP6/hfls/hfls.all_annual.txt")
hfss.all_annual <- read.table("CMIP6/hfss/hfss.all_annual.txt")
hurs.all_annual <- read.table("CMIP6/hurs/hurs.all_annual.txt")
cmip6_annual <- tas.all_annual %>% merge(pr.all_annual, by = c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_annual, by = c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_annual, by = c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_annual, by = c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_annual, by = c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_annual, "cmip6_annual.txt")tas.all_january <- read.table("CMIP6/tas/tas.all_january.txt")
pr.all_january <- read.table("CMIP6/pr/pr.all_january.txt")
sfcWind.all_january <- read.table("CMIP6/sfcWind/sfcWind.all_january.txt")
hfls.all_january <- read.table("CMIP6/hfls/hfls.all_january.txt")
hfss.all_january <- read.table("CMIP6/hfss/hfss.all_january.txt")
hurs.all_january <- read.table("CMIP6/hurs/hurs.all_january.txt")
cmip6_january <- tas.all_january %>% merge(pr.all_january, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_january, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_january, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_january, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_january, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_january, "cmip6_january.txt")tas.all_february <- read.table("CMIP6/tas/tas.all_february.txt")
pr.all_february <- read.table("CMIP6/pr/pr.all_february.txt")
sfcWind.all_february <- read.table("CMIP6/sfcWind/sfcWind.all_february.txt")
hfls.all_february <- read.table("CMIP6/hfls/hfls.all_february.txt")
hfss.all_february <- read.table("CMIP6/hfss/hfss.all_february.txt")
hurs.all_february <- read.table("CMIP6/hurs/hurs.all_february.txt")
cmip6_february <- tas.all_february %>% merge(pr.all_february, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_february, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_february, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_february, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_february, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_february, "cmip6_february.txt")tas.all_march <- read.table("CMIP6/tas/tas.all_march.txt")
pr.all_march <- read.table("CMIP6/pr/pr.all_march.txt")
sfcWind.all_march <- read.table("CMIP6/sfcWind/sfcWind.all_march.txt")
hfls.all_march <- read.table("CMIP6/hfls/hfls.all_march.txt")
hfss.all_march <- read.table("CMIP6/hfss/hfss.all_march.txt")
hurs.all_march <- read.table("CMIP6/hurs/hurs.all_march.txt")
cmip6_march <- tas.all_march %>% merge(pr.all_march, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_march, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_march, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_march, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_march, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_march, "cmip6_march.txt")tas.all_april <- read.table("CMIP6/tas/tas.all_april.txt")
pr.all_april <- read.table("CMIP6/pr/pr.all_april.txt")
sfcWind.all_april <- read.table("CMIP6/sfcWind/sfcWind.all_april.txt")
hfls.all_april <- read.table("CMIP6/hfls/hfls.all_april.txt")
hfss.all_april <- read.table("CMIP6/hfss/hfss.all_april.txt")
hurs.all_april <- read.table("CMIP6/hurs/hurs.all_april.txt")
cmip6_april <- tas.all_april %>% merge(pr.all_april, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_april, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_april, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_april, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_april, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_april, "cmip6_april.txt")tas.all_may <- read.table("CMIP6/tas/tas.all_may.txt")
pr.all_may <- read.table("CMIP6/pr/pr.all_may.txt")
sfcWind.all_may <- read.table("CMIP6/sfcWind/sfcWind.all_may.txt")
hfls.all_may <- read.table("CMIP6/hfls/hfls.all_may.txt")
hfss.all_may <- read.table("CMIP6/hfss/hfss.all_may.txt")
hurs.all_may <- read.table("CMIP6/hurs/hurs.all_may.txt")
cmip6_may <- tas.all_may %>% merge(pr.all_may, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_may, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_may, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_may, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_may, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_may, "cmip6_may.txt")tas.all_june <- read.table("CMIP6/tas/tas.all_june.txt")
pr.all_june <- read.table("CMIP6/pr/pr.all_june.txt")
sfcWind.all_june <- read.table("CMIP6/sfcWind/sfcWind.all_june.txt")
hfls.all_june <- read.table("CMIP6/hfls/hfls.all_june.txt")
hfss.all_june <- read.table("CMIP6/hfss/hfss.all_june.txt")
hurs.all_june <- read.table("CMIP6/hurs/hurs.all_june.txt")
cmip6_june <- tas.all_june %>% merge(pr.all_june, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_june, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_june, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_june, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_june, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_june, "cmip6_june.txt")tas.all_july <- read.table("CMIP6/tas/tas.all_july.txt")
pr.all_july <- read.table("CMIP6/pr/pr.all_july.txt")
sfcWind.all_july <- read.table("CMIP6/sfcWind/sfcWind.all_july.txt")
hfls.all_july <- read.table("CMIP6/hfls/hfls.all_july.txt")
hfss.all_july <- read.table("CMIP6/hfss/hfss.all_july.txt")
hurs.all_july <- read.table("CMIP6/hurs/hurs.all_july.txt")
cmip6_july <- tas.all_july %>% merge(pr.all_july, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_july, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_july, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_july, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_july, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_july, "cmip6_july.txt")tas.all_august <- read.table("CMIP6/tas/tas.all_august.txt")
pr.all_august <- read.table("CMIP6/pr/pr.all_august.txt")
sfcWind.all_august <- read.table("CMIP6/sfcWind/sfcWind.all_august.txt")
hfls.all_august <- read.table("CMIP6/hfls/hfls.all_august.txt")
hfss.all_august <- read.table("CMIP6/hfss/hfss.all_august.txt")
hurs.all_august <- read.table("CMIP6/hurs/hurs.all_august.txt")
cmip6_august <- tas.all_august %>% merge(pr.all_august, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_august, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_august, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_august, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_august, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_august, "cmip6_august.txt")tas.all_september <- read.table("CMIP6/tas/tas.all_september.txt")
pr.all_september <- read.table("CMIP6/pr/pr.all_september.txt")
sfcWind.all_september <- read.table("CMIP6/sfcWind/sfcWind.all_september.txt")
hfls.all_september <- read.table("CMIP6/hfls/hfls.all_september.txt")
hfss.all_september <- read.table("CMIP6/hfss/hfss.all_september.txt")
hurs.all_september <- read.table("CMIP6/hurs/hurs.all_september.txt")
cmip6_september <- tas.all_september %>% merge(pr.all_september, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_september, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_september, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_september, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_september, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_september, "cmip6_september.txt")tas.all_october <- read.table("CMIP6/tas/tas.all_october.txt")
pr.all_october <- read.table("CMIP6/pr/pr.all_october.txt")
sfcWind.all_october <- read.table("CMIP6/sfcWind/sfcWind.all_october.txt")
hfls.all_october <- read.table("CMIP6/hfls/hfls.all_october.txt")
hfss.all_october <- read.table("CMIP6/hfss/hfss.all_october.txt")
hurs.all_october <- read.table("CMIP6/hurs/hurs.all_october.txt")
cmip6_october <- tas.all_october %>% merge(pr.all_october, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_october, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_october, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_october, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_october, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_october, "cmip6_october.txt")tas.all_november <- read.table("CMIP6/tas/tas.all_november.txt")
pr.all_november <- read.table("CMIP6/pr/pr.all_november.txt")
sfcWind.all_november <- read.table("CMIP6/sfcWind/sfcWind.all_november.txt")
hfls.all_november <- read.table("CMIP6/hfls/hfls.all_november.txt")
hfss.all_november <- read.table("CMIP6/hfss/hfss.all_november.txt")
hurs.all_november <- read.table("CMIP6/hurs/hurs.all_november.txt")
cmip6_november <- tas.all_november %>% merge(pr.all_november, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_november, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_november, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_november, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_november, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_november, "cmip6_november.txt")tas.all_december <- read.table("CMIP6/tas/tas.all_december.txt")
pr.all_december <- read.table("CMIP6/pr/pr.all_december.txt")
sfcWind.all_december <- read.table("CMIP6/sfcWind/sfcWind.all_december.txt")
hfls.all_december <- read.table("CMIP6/hfls/hfls.all_december.txt")
hfss.all_december <- read.table("CMIP6/hfss/hfss.all_december.txt")
hurs.all_december <- read.table("CMIP6/hurs/hurs.all_december.txt")
cmip6_december <- tas.all_december %>% merge(pr.all_december, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_december, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_december, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_december, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_december, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_december, "cmip6_december.txt")2 Aridity Index
2.1 De Martonne
cmip6 <- read.table("cmip6_annual.txt")
breaks.martonne <- c(0,5,10,20,"30","40",Inf)
cat.martonne <- c("[0,5]" = "Desert", "(5,10]"= "Arid", "(10,20]" = "Semi-arid","(20,30]" = "Temperate", "(30,40]" = "Humid", "(40,Inf]" = "Forest")
col.martonne <- c("Desert" = colorvec[2], "Arid"= colorvec[4],"Semi-arid"= colorvec[5], "Temperate" = colorvec[8], "Humid"= colorvec[10],"Forest"= colorvec[11])
cmip6$AIm <- with(cmip6,pr*60*60*24*365/(tas-273.15+10)) # pr in mm/y, tas in Ceslsius
cmip6$cat.AIm <- cmip6$AIm %>% cut(breaks.martonne, include.lowest = T) %>% revalue(cat.martonne)
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "1970_2000" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AIm))+
scale_fill_manual(values = col.martonne, na.translate = F)+
labs(title = paste(i, "1970-2000", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
2.2 UNEP
ea from Rh:
ea = 6.108(RH/100)exp(17.27*T/(T+237.3)) Refs https://www.nature.com/articles/s41598-023-40499-6 : Allen and Tetens
id_vars <- c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")
cmip6_annual <- mutate(read.table("cmip6_annual.txt"),
t = tas - 273.15,
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_annual = ((0.408*delta*Rn+gamma*900/((t)+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_01 <- mutate(read.table("cmip6_january.txt"),
t = tas - 273.15,
pr_01 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_01 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind)))) # Evapotranspiration, G considered to be negligible. in mm/day. Wind speed at 10 m is converted to wind speed at 2 m by multiplying by 0.748
cmip6_02 <- mutate(read.table("cmip6_february.txt"),
t = tas - 273.15,
pr_02 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_02 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_03 <- mutate(read.table("cmip6_march.txt"),
t = tas - 273.15,
pr_03 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_03 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_04 <- mutate(read.table("cmip6_april.txt"),
t = tas - 273.15,
pr_04 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_04 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_05 <- mutate(read.table("cmip6_may.txt"),
t = tas - 273.15,
pr_05 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_05 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_06 <- mutate(read.table("cmip6_june.txt"),
t = tas - 273.15,
pr_06 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_06 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_07 <- mutate(read.table("cmip6_july.txt"),
t = tas - 273.15,
pr_07 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_07 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_08 <- mutate(read.table("cmip6_august.txt"),
t = tas - 273.15,
pr_08 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_08 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_09 <- mutate(read.table("cmip6_september.txt"),
t = tas - 273.15,
pr_09 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_09 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_10 <- mutate(read.table("cmip6_october.txt"),
t = tas - 273.15,
pr_10 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_10 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_11 <- mutate(read.table("cmip6_november.txt"),
t = tas - 273.15,
pr_11 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_11 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_12 <- mutate(read.table("cmip6_december.txt"),
t = tas - 273.15,
pr_12 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_12 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
et0_monthly <- select(cmip6_annual, id_vars) %>%
merge(select(cmip6_01, c(id_vars, "ET0_01", "pr_01")), by = id_vars) %>%
merge(select(cmip6_02, c(id_vars, "ET0_02", "pr_02")), by = id_vars) %>%
merge(select(cmip6_03, c(id_vars, "ET0_03", "pr_03")), by = id_vars) %>%
merge(select(cmip6_04, c(id_vars, "ET0_04", "pr_04")), by = id_vars) %>%
merge(select(cmip6_05, c(id_vars, "ET0_05", "pr_05")), by = id_vars) %>%
merge(select(cmip6_06, c(id_vars, "ET0_06", "pr_06")), by = id_vars) %>%
merge(select(cmip6_07, c(id_vars, "ET0_07", "pr_07")), by = id_vars) %>%
merge(select(cmip6_08, c(id_vars, "ET0_08", "pr_08")), by = id_vars) %>%
merge(select(cmip6_09, c(id_vars, "ET0_09", "pr_09")), by = id_vars) %>%
merge(select(cmip6_10, c(id_vars, "ET0_10", "pr_10")), by = id_vars) %>%
merge(select(cmip6_11, c(id_vars, "ET0_11", "pr_11")), by = id_vars) %>%
merge(select(cmip6_12, c(id_vars, "ET0_12", "pr_12")), by = id_vars)
et0_monthly$spr <- with(et0_monthly, pr_01*31 + pr_02 * 28 + pr_03 * 31 + pr_04 * 30 + pr_05 * 31 + pr_06 * 30 +
pr_07 * 31 + pr_08 * 31 + pr_09 * 30 + pr_10 * 31 + pr_11 * 30 + pr_12 * 31)
et0_monthly$sET0 <- with(et0_monthly, ET0_01*31 + ET0_02 * 28 + ET0_03 * 31 + ET0_04 * 30 + ET0_05 * 31 + ET0_06 * 30 +
ET0_07 * 31 + ET0_08 * 31 + ET0_09 * 30 + ET0_10 * 31 + ET0_11 * 30 + ET0_12 * 31)
cmip6 <- merge(cmip6_annual, et0_monthly, by = id_vars)
cmip6$AI <- with(cmip6, abs(spr/sET0))
cmip6$AI_annual <- with(cmip6, abs((pr*60*60*24*365)/(ET0_annual*365)))
breaks.unesco <- c(-Inf,0,0.03,0.2,0.5,0.65,Inf)
cat.unesco <- c("(0,0.03]"= "Hyperarid", "(0.03,0.2]" = "Arid", "(0.2,0.5]" = "Semi-arid", "(0.5,0.65]" = "Dry subhumid","(0.65, Inf]" = "Humid", "(-Inf,0]" = "Cold")
col.cat <- c("Hyperarid" = colorvec[2], "Arid"= colorvec[4],"Semi-arid"= colorvec[5], "Dry subhumid" = colorvec[8], "Humid"= colorvec[10], "Cold" = "powderblue")
cmip6$cat.AI <- cmip6$AI %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6$cat.AI[which(cmip6$sET0 < 400)] <- "Cold"
cmip6$cat.AI %>% unique()
cmip6$cat.AI <- cmip6$AI %>% cut(breaks = breaks.unesco) %>%
revalue(cat.unesco)
cmip6$cat.AI_annual <- cmip6$AI_annual %>% cut(breaks = breaks.unesco) %>%
revalue(cat.unesco)
cmip6$cat.AI_annual[which(cmip6$ET0_annual*365 < 400)] <- "Cold"
write.table(cmip6, "cmip6_AI.txt")
cmip6 <- read.table("cmip6_AI.txt")
cmip6_AI <- cmip6 %>% subset(period == "1970_2000") %>%
group_by(lon,lat,Continent, Type, Name, Acronym, lm, model, z) %>%
dplyr::summarise(tas = mean(tas, na.rm = T) - 273.15, pr = mean(pr, na.rm = T) * 60 * 60 * 24 * 365,
sfcWind = mean(sfcWind, na.rm = T), Rn = mean(Rn, na.rm = T),
ea = mean(ea, na.rm = T), ET0 = mean(sET0, na.rm = T), AI = mean(AI, na.rm = T)) %>%
ungroup() %>%
mutate(source = "CMIP6")%>%
select(c("lon","lat","Continent","Type","Name","Acronym","lm","model","source","pr","tas","sfcWind","Rn", "ea","z","ET0","AI"))
write.table(cmip6_AI, "cmip6ref.txt")3 Maps AI by model
cmip6 <- read.table("cmip6_AI.txt")
breaks.unesco <- c(-Inf,0.03,0.2,0.5,0.65,Inf)
cat.unesco <- c("(-Inf,0.03]"= "Hyperarid", "(0.03,0.2]" = "Arid", "(0.2,0.5]" = "Semi-arid", "(0.5,0.65]" = "Dry subhumid","(0.65, Inf]" = "Humid")
col.cat <- c("Hyperarid" = colorvec[2], "Arid"= colorvec[4],"Semi-arid"= colorvec[5], "Dry subhumid" = colorvec[8], "Humid"= colorvec[10], "Cold" = "powderblue")3.1 1850-1880
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "1850_1880" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "1850-1880", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "1850_1880" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "1850_1880" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 3.6 | 6.3 | 9.2 | 4.3 | 23.4 | 26.4 | 47.7 | 2.6 |
| CESM | 3.6 | 7.5 | 10.2 | 3.5 | 24.8 | 24.8 | 47.8 | 2.6 |
| CMCC | 3.9 | 5.0 | 6.6 | 3.7 | 19.2 | 29.3 | 48.9 | 2.6 |
| CMCC-ESM2 | 4.2 | 5.7 | 5.2 | 1.9 | 17.0 | 16.7 | 63.6 | 2.6 |
| CNRM | 4.0 | 8.4 | 8.0 | 4.6 | 25.0 | 26.4 | 46.0 | 2.6 |
| EC-Earth3 | 6.0 | 6.2 | 7.6 | 3.1 | 22.9 | 25.6 | 48.9 | 2.6 |
| FGOALS | 3.9 | 8.9 | 11.0 | 5.0 | 28.8 | 20.8 | 47.8 | 2.6 |
| GFDL-ESM4 | 3.9 | 6.5 | 6.1 | 2.9 | 19.4 | 25.8 | 52.3 | 2.6 |
| INM | 3.0 | 5.7 | 9.4 | 4.0 | 22.1 | 28.2 | 47.0 | 2.6 |
| INM-CM5 | 2.4 | 6.2 | 8.0 | 3.9 | 20.5 | 30.0 | 46.9 | 2.6 |
| MPI | 5.9 | 7.6 | 7.0 | 3.0 | 23.5 | 24.0 | 49.9 | 2.6 |
| MRI | 4.4 | 7.4 | 6.0 | 2.7 | 20.5 | 27.5 | 49.5 | 2.6 |
| NorESM-2-MM | 3.6 | 7.3 | 12.8 | 4.1 | 27.8 | 24.7 | 45.0 | 2.6 |
| mean | 4.0 | 6.8 | 8.2 | 3.6 | 22.6 | 25.4 | 49.3 | 2.6 |
| sd | 1.0 | 1.1 | 2.2 | 0.9 | 5.2 | 3.5 | 4.7 | 0.0 |
3.2 1970-2000
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "1970_2000" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "1850-1880", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
library(ggrepel)
pie <- cmip6 %>% subset(period == "1970_2000" & !Continent %in% c("SOUTHERN","PACIFIC","POLAR","ATLANTIC","INDIAN","ARCTIC")) %>%
select(c("source", "cat.AI")) %>%
reshape2::melt(id.vars = c("source")) %>%
group_by(source, value) %>%
summarise(count = n()) %>%
mutate(perc = round(count/sum(count)*100, 1),
lab.y = rev(cumsum(rev(count))) - count*0.5) %>%
ungroup()
pie_list <- list()
for(i in unique(pie$source)){
p <- ggplot(subset(pie, source == i))+
geom_bar(aes(x = "", y = count, fill = value), width = 1, stat = "identity")+
coord_polar("y", start = 0)+
scale_fill_manual(values = col.cat)+
geom_label_repel(aes(x = "", y = lab.y, label = perc), nudge_x = 0.5)+
labs(fill = "", title = i)+
theme_void()+theme(legend.position = "right")
pie_list[[i]] <- p
}
ggpubr::ggarrange(plotlist = pie_list, nrow = 4, ncol = 4, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "1970_2000" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "1970_2000" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 3.6 | 6.0 | 9.2 | 4.4 | 23.2 | 26.0 | 48.2 | 2.6 |
| CESM | 3.7 | 7.4 | 9.8 | 3.6 | 24.5 | 24.3 | 48.5 | 2.6 |
| CMCC | 3.6 | 5.2 | 7.0 | 3.8 | 19.6 | 29.4 | 48.5 | 2.6 |
| CMCC-ESM2 | 4.5 | 5.5 | 5.3 | 1.8 | 17.1 | 17.0 | 63.3 | 2.6 |
| CNRM | 4.0 | 8.5 | 7.9 | 4.5 | 24.9 | 26.6 | 45.9 | 2.6 |
| EC-Earth3 | 5.9 | 6.6 | 7.5 | 3.1 | 23.1 | 24.8 | 49.6 | 2.6 |
| FGOALS | 4.1 | 7.6 | 7.0 | 3.4 | 22.1 | 17.5 | 57.9 | 2.6 |
| GFDL-ESM4 | 4.0 | 6.2 | 6.2 | 2.8 | 19.2 | 24.6 | 53.6 | 2.6 |
| INM | 3.1 | 5.9 | 9.2 | 4.0 | 22.2 | 27.9 | 47.4 | 2.6 |
| INM-CM5 | 2.3 | 5.9 | 8.4 | 3.9 | 20.5 | 29.9 | 47.0 | 2.6 |
| MPI | 6.0 | 7.7 | 7.1 | 3.0 | 23.8 | 23.7 | 49.9 | 2.6 |
| MRI | 4.3 | 7.5 | 6.4 | 2.7 | 20.9 | 27.0 | 49.5 | 2.6 |
| NorESM-2-MM | 3.6 | 7.0 | 11.9 | 3.9 | 26.4 | 23.3 | 47.7 | 2.6 |
| mean | 4.1 | 6.7 | 7.9 | 3.5 | 22.2 | 24.8 | 50.5 | 2.6 |
| sd | 1.0 | 1.0 | 1.8 | 0.8 | 4.6 | 3.9 | 5.0 | 0.0 |
3.3 1985-2010
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "1985_2015" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "1985-2010", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "1985_2015" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "1985_2015" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 3.7 | 5.9 | 9.6 | 4.6 | 23.8 | 25.9 | 47.7 | 2.6 |
| CESM | 3.3 | 7.5 | 10.3 | 3.7 | 24.8 | 24.6 | 47.8 | 2.6 |
| CMCC | 3.6 | 5.3 | 7.2 | 4.0 | 20.1 | 29.8 | 47.4 | 2.6 |
| CMCC-ESM2 | 4.3 | 5.9 | 5.4 | 1.7 | 17.3 | 17.1 | 63.0 | 2.6 |
| CNRM | 4.0 | 8.4 | 7.9 | 4.3 | 24.6 | 27.3 | 45.5 | 2.6 |
| EC-Earth3 | 5.8 | 6.8 | 7.4 | 3.1 | 23.1 | 25.6 | 48.8 | 2.6 |
| FGOALS | 4.0 | 7.9 | 7.2 | 3.4 | 22.5 | 17.6 | 57.3 | 2.6 |
| GFDL-ESM4 | 4.1 | 6.3 | 6.3 | 2.7 | 19.4 | 25.4 | 52.6 | 2.6 |
| INM | 2.8 | 5.4 | 7.9 | 3.6 | 19.7 | 25.4 | 52.3 | 2.6 |
| INM-CM5 | 2.4 | 5.8 | 8.3 | 3.8 | 20.3 | 28.3 | 48.8 | 2.6 |
| MPI | 6.1 | 7.8 | 7.2 | 3.1 | 24.2 | 23.7 | 49.6 | 2.6 |
| MRI | 4.3 | 7.5 | 6.3 | 2.6 | 20.7 | 26.7 | 50.0 | 2.6 |
| NorESM-2-MM | 3.3 | 6.9 | 11.1 | 3.9 | 25.2 | 21.1 | 51.1 | 2.6 |
| mean | 4.0 | 6.7 | 7.9 | 3.4 | 22.0 | 24.5 | 50.9 | 2.6 |
| sd | 1.0 | 1.0 | 1.6 | 0.8 | 4.4 | 3.8 | 4.7 | 0.0 |
3.4 SSP 2-4.5
3.4.1 2030-2060
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2030_2060" & model == "SSP245" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2030-2060", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2030_2060" & model == "SSP245" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2030_2060" & model == "SSP245" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 3.5 | 6.4 | 10.2 | 4.6 | 24.7 | 26.5 | 46.2 | 2.6 |
| CESM | 3.3 | 8.6 | 10.6 | 3.9 | 26.4 | 24.8 | 46.2 | 2.6 |
| CMCC | 3.1 | 5.9 | 7.6 | 4.2 | 20.8 | 30.8 | 45.8 | 2.6 |
| CMCC-ESM2 | 4.2 | 6.3 | 5.5 | 2.0 | 18.0 | 17.6 | 61.7 | 2.6 |
| CNRM | 4.2 | 8.8 | 8.4 | 4.7 | 26.1 | 27.7 | 43.6 | 2.6 |
| EC-Earth3 | 5.2 | 6.4 | 7.7 | 3.1 | 22.4 | 25.3 | 49.7 | 2.6 |
| FGOALS | 4.8 | 9.3 | 12.9 | 5.4 | 32.4 | 21.1 | 44.0 | 2.6 |
| GFDL-ESM4 | 3.8 | 7.1 | 6.6 | 3.1 | 20.6 | 25.7 | 51.3 | 2.6 |
| INM | 3.2 | 5.8 | 9.6 | 4.1 | 22.7 | 28.1 | 46.5 | 2.6 |
| INM-CM5 | 2.5 | 6.0 | 8.5 | 3.8 | 20.8 | 30.0 | 46.7 | 2.6 |
| MPI | 6.2 | 8.3 | 7.7 | 3.1 | 25.3 | 24.5 | 47.6 | 2.6 |
| MRI | 4.3 | 7.8 | 6.5 | 2.8 | 21.4 | 27.8 | 48.1 | 2.6 |
| NorESM-2-MM | 3.7 | 8.2 | 11.7 | 3.9 | 27.5 | 23.3 | 46.5 | 2.6 |
| mean | 4.0 | 7.3 | 8.7 | 3.7 | 23.7 | 25.6 | 48.0 | 2.6 |
| sd | 1.0 | 1.2 | 2.2 | 0.9 | 5.3 | 3.6 | 4.6 | 0.0 |
3.4.2 2070-2100
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2070_2100" & model == "SSP245" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2070-2100", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2070_2100" & model == "SSP245" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2070_2100" & model == "SSP245" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 3.3 | 7.3 | 10.4 | 5.1 | 26.1 | 26.6 | 44.8 | 2.6 |
| CESM | 4.2 | 8.1 | 10.7 | 3.7 | 26.7 | 25.7 | 44.9 | 2.6 |
| CMCC | 3.0 | 5.6 | 8.5 | 4.5 | 21.6 | 32.4 | 43.4 | 2.6 |
| CMCC-ESM2 | 4.3 | 6.6 | 5.9 | 1.9 | 18.7 | 18.7 | 60.0 | 2.6 |
| CNRM | 4.4 | 9.0 | 8.6 | 4.7 | 26.7 | 28.5 | 42.2 | 2.6 |
| EC-Earth3 | 5.3 | 6.4 | 7.7 | 3.0 | 22.4 | 26.0 | 49.1 | 2.6 |
| FGOALS | 4.8 | 9.8 | 12.6 | 5.6 | 32.8 | 21.1 | 43.5 | 2.6 |
| GFDL-ESM4 | 4.1 | 6.7 | 6.8 | 3.1 | 20.7 | 26.8 | 50.0 | 2.6 |
| INM | 3.3 | 5.7 | 9.8 | 4.1 | 22.9 | 28.3 | 46.3 | 2.6 |
| INM-CM5 | 2.6 | 6.6 | 7.8 | 4.1 | 21.1 | 30.3 | 46.0 | 2.6 |
| MPI | 6.2 | 8.3 | 7.7 | 3.1 | 25.3 | 24.5 | 47.6 | 2.6 |
| MRI | 4.5 | 7.6 | 6.7 | 2.8 | 21.6 | 28.4 | 47.4 | 2.6 |
| NorESM-2-MM | 4.1 | 8.2 | 11.8 | 4.1 | 28.2 | 23.7 | 45.6 | 2.6 |
| mean | 4.2 | 7.4 | 8.8 | 3.8 | 24.2 | 26.2 | 47.0 | 2.6 |
| sd | 1.0 | 1.3 | 2.1 | 1.0 | 5.4 | 3.7 | 4.5 | 0.0 |
3.5 SSP 3-7.0
3.5.1 2030-2060
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2030_2060" & model == "SSP370" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2030-2060", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2030_2060" & model == "SSP370" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2030_2060" & model == "SSP370" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 3.4 | 6.7 | 9.9 | 4.8 | 24.8 | 26.0 | 46.6 | 2.6 |
| CESM | 3.6 | 7.9 | 10.8 | 3.7 | 26.0 | 24.8 | 46.4 | 2.6 |
| CMCC | 2.9 | 5.8 | 7.9 | 4.1 | 20.7 | 30.7 | 46.0 | 2.6 |
| CMCC-ESM2 | 2.3 | 5.8 | 6.2 | 2.5 | 16.8 | 18.7 | 61.8 | 2.6 |
| CNRM | 4.3 | 9.0 | 8.9 | 5.0 | 27.2 | 27.6 | 42.6 | 2.6 |
| EC-Earth3 | 5.5 | 6.8 | 8.0 | 3.3 | 23.6 | 26.8 | 47.0 | 2.6 |
| FGOALS | 4.2 | 8.3 | 8.3 | 3.7 | 24.5 | 19.2 | 53.8 | 2.6 |
| GFDL-ESM4 | 4.4 | 6.6 | 6.9 | 2.9 | 20.8 | 25.0 | 51.8 | 2.6 |
| INM | 3.2 | 5.8 | 9.4 | 4.1 | 22.5 | 28.0 | 46.9 | 2.6 |
| INM-CM5 | 2.4 | 6.2 | 8.4 | 3.9 | 20.9 | 30.1 | 46.4 | 2.6 |
| MPI | 6.1 | 8.5 | 7.6 | 3.1 | 25.3 | 25.3 | 46.8 | 2.6 |
| MRI | 4.5 | 7.5 | 6.5 | 2.5 | 21.0 | 28.0 | 48.3 | 2.6 |
| NorESM-2-MM | 3.8 | 7.6 | 12.5 | 4.0 | 27.9 | 23.1 | 46.4 | 2.6 |
| mean | 3.9 | 7.1 | 8.6 | 3.7 | 23.3 | 25.6 | 48.5 | 2.6 |
| sd | 1.1 | 1.1 | 1.8 | 0.8 | 4.8 | 3.6 | 4.8 | 0.0 |
3.5.2 2070-2100
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2070_2100" & model == "SSP370" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2070-2100", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2070_2100" & model == "SSP370" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2070_2100" & model == "SSP370" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 3.8 | 7.3 | 10.7 | 4.8 | 26.6 | 26.0 | 44.9 | 2.6 |
| CESM | 4.2 | 8.5 | 10.8 | 4.0 | 27.5 | 26.0 | 43.9 | 2.6 |
| CMCC | 3.6 | 6.3 | 8.7 | 5.1 | 23.7 | 30.7 | 43.0 | 2.6 |
| CMCC-ESM2 | 4.8 | 6.9 | 5.7 | 2.1 | 19.5 | 18.0 | 59.9 | 2.6 |
| CNRM | 5.1 | 9.0 | 9.2 | 4.7 | 28.0 | 27.3 | 42.2 | 2.6 |
| EC-Earth3 | 5.3 | 6.9 | 8.6 | 3.3 | 24.1 | 28.1 | 45.3 | 2.6 |
| FGOALS | 4.7 | 9.3 | 9.2 | 3.0 | 26.2 | 19.5 | 51.8 | 2.6 |
| GFDL-ESM4 | 5.2 | 5.9 | 7.6 | 3.3 | 22.0 | 25.7 | 49.7 | 2.6 |
| INM | 4.2 | 5.7 | 10.2 | 4.3 | 24.4 | 27.3 | 45.7 | 2.6 |
| INM-CM5 | 2.7 | 7.2 | 9.8 | 4.3 | 24.0 | 28.1 | 45.3 | 2.6 |
| MPI | 6.1 | 8.5 | 7.6 | 3.1 | 25.3 | 25.3 | 46.8 | 2.6 |
| MRI | 5.0 | 8.3 | 6.9 | 2.9 | 23.1 | 27.2 | 47.2 | 2.6 |
| NorESM-2-MM | 4.1 | 8.2 | 12.2 | 4.1 | 28.6 | 23.5 | 45.3 | 2.6 |
| mean | 4.5 | 7.5 | 9.0 | 3.8 | 24.8 | 25.6 | 47.0 | 2.6 |
| sd | 0.9 | 1.2 | 1.8 | 0.9 | 4.8 | 3.5 | 4.7 | 0.0 |
3.6 SSP 5-8.5
3.6.1 2030-2060
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2030_2060" & model == "SSP585" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2030-2060", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2030_2060" & model == "SSP585" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2030_2060" & model == "SSP585" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 3.4 | 6.4 | 10.5 | 5.2 | 25.5 | 26.2 | 45.8 | 2.6 |
| CESM | 4.0 | 8.0 | 10.9 | 3.6 | 26.5 | 25.0 | 45.9 | 2.6 |
| CMCC | 3.0 | 5.6 | 8.0 | 4.5 | 21.1 | 31.2 | 45.0 | 2.6 |
| CMCC-ESM2 | 4.5 | 6.5 | 5.6 | 1.7 | 18.3 | 17.7 | 61.4 | 2.6 |
| CNRM | 4.3 | 9.2 | 8.6 | 4.8 | 26.9 | 27.8 | 42.6 | 2.6 |
| EC-Earth3 | 5.4 | 6.6 | 8.2 | 3.2 | 23.4 | 27.0 | 47.0 | 2.6 |
| FGOALS | 4.8 | 9.4 | 12.6 | 5.4 | 32.2 | 21.4 | 43.9 | 2.6 |
| GFDL-ESM4 | 4.3 | 6.6 | 6.8 | 3.3 | 21.0 | 25.3 | 51.2 | 2.6 |
| INM | 3.2 | 5.8 | 9.7 | 4.1 | 22.8 | 28.3 | 46.2 | 2.6 |
| INM-CM5 | 2.5 | 6.0 | 8.6 | 4.0 | 21.1 | 30.3 | 46.0 | 2.6 |
| MPI | 6.0 | 8.7 | 7.7 | 3.1 | 25.5 | 25.7 | 46.2 | 2.6 |
| MRI | 4.2 | 7.7 | 6.7 | 2.8 | 21.4 | 28.4 | 47.6 | 2.6 |
| NorESM-2-MM | 3.8 | 7.9 | 12.2 | 4.1 | 28.0 | 23.1 | 46.2 | 2.6 |
| mean | 4.1 | 7.3 | 8.9 | 3.8 | 24.1 | 26.0 | 47.3 | 2.6 |
| sd | 1.0 | 1.3 | 2.1 | 1.0 | 5.4 | 3.7 | 4.7 | 0.0 |
3.6.2 2070-2100
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2070_2100" & model == "SSP585" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2070-2100", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2070_2100" & model == "SSP585" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2070_2100" & model == "SSP585" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(13,14), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 4.3 | 7.5 | 11.1 | 4.5 | 27.4 | 26.1 | 43.9 | 2.6 |
| CMCC | 3.1 | 6.5 | 9.4 | 4.6 | 23.6 | 32.5 | 41.3 | 2.6 |
| CMCC-ESM2 | 3.9 | 7.3 | 6.2 | 2.0 | 19.4 | 19.5 | 58.5 | 2.6 |
| CNRM | 4.2 | 9.4 | 8.9 | 4.4 | 26.9 | 28.5 | 42.0 | 2.6 |
| EC-Earth3 | 5.4 | 6.7 | 9.1 | 3.4 | 24.6 | 28.2 | 44.6 | 2.6 |
| FGOALS | 10.8 | 10.4 | 12.7 | 4.7 | 38.6 | 16.9 | 41.8 | 2.6 |
| GFDL-ESM4 | 4.5 | 6.9 | 7.5 | 3.3 | 22.2 | 26.2 | 49.0 | 2.6 |
| INM | 3.1 | 6.1 | 10.3 | 4.1 | 23.6 | 29.0 | 44.8 | 2.6 |
| INM-CM5 | 2.7 | 6.8 | 8.3 | 4.2 | 22.0 | 30.7 | 44.6 | 2.6 |
| MPI | 6.1 | 8.6 | 7.7 | 3.3 | 25.7 | 25.6 | 46.2 | 2.6 |
| MRI | 4.9 | 8.0 | 7.4 | 2.8 | 23.1 | 27.9 | 46.4 | 2.6 |
| NorESM-2-MM | 4.4 | 8.9 | 12.1 | 4.5 | 29.9 | 23.9 | 43.5 | 2.6 |
| mean | 4.8 | 7.8 | 9.2 | 3.8 | 25.6 | 26.2 | 45.5 | 2.6 |
| sd | 2.1 | 1.3 | 2.0 | 0.8 | 6.2 | 4.4 | 4.6 | 0.0 |
4 Agreement between models
4.1 Summary dataframe
getAIref <- function(i) {
AIrefi = subset(cmip6s, lat == cmip6s$lat[i] & lon == cmip6s$lon[i] & period =="1970_2000") %>% pull(AI.mean)
return(AIrefi)
}
AI.refbymodel <- sapply(as.numeric(rownames(cmip6)), FUN = getAIref)
saveRDS(AI.ref.bymodel, "AI.ref.bymodel.rds")
cmip6ref <- cmip6 %>% select(c("lon","lat","model","AI")) %>% subset(period = "1970-2000") %>% setNames(c("lon","lat","model","AI.ref"))
test <- merge(head(cmip6, 5000), cmip6ref, by = c("lon","lat","model"))
cmip6s <- cmip6 %>%
group_by(lon,lat,Continent, Type, Name, Acronym, lm, model, period, z) %>%
dplyr::summarise(t.mean = mean(t, na.rm = T), t.sd = sd(t, na.rm = T), # t in °C
pr.mean = mean(spr, na.rm = T) , pr.sd = sd(spr, na.rm = T), # pr in mm/y
sfcWind.mean = mean(sfcWind, na.rm = T), sfcWind.sd = sd(sfcWind, na.rm = T),
Rn.mean = mean(Rn, na.rm = T), Rn.sd = sd(Rn, na.rm = T), # in MJ/m2/day
ea.mean = mean(ea, na.rm = T), ea.sd = sd(ea, na.rm = T), # in kPa
ET0 = mean(sET0, na.rm = T), ET0.sd = sd(sET0, na.rm = T), # in mm/y
AI.mean = mean(AI, na.rm = T), AI.sd = sd(AI, na.rm = T),
hyperarid = sum(cat.AI == "Hyperarid"),
arid = sum(cat.AI == "Arid"),
semiarid = sum(cat.AI == "Semi-arid"),
drysubhumid = sum(cat.AI == "Dry subhumid") ,
humid = sum(cat.AI == "Humid"),
cold = sum(cat.AI == "Cold"),
na = sum(is.na(cat.AI)),
nb.models = n(),
nb.maj = max(hyperarid,arid,semiarid,drysubhumid,humid,cold,na),
prop.maj = nb.maj/nb.models,
) %>% ungroup() %>% as.data.frame()
cmip6s$cat.maj <- colnames(select(cmip6s, c("hyperarid","arid",'semiarid',"drysubhumid",'humid',"cold","na")))[max.col(select(cmip6s, c("hyperarid","arid",'semiarid',"drysubhumid",'humid',"cold","na")),ties.method = "first")]
write.table(cmip6s, "cmip6s.txt")cmip6s <- read.table("cmip6s.txt")
breaks.unesco <- c(-Inf,0,0.03,0.2,0.5,0.65,Inf)
cat.unesco <- c("(0,0.03]"= "Hyperarid", "(0.03,0.2]" = "Arid", "(0.2,0.5]" = "Semi-arid", "(0.5,0.65]" = "Dry subhumid","(0.65, Inf]" = "Humid", "(-Inf,0]" = "Cold")
cmip6s$cat.AI <- cmip6s$AI.mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6s$cat.AI[which(cmip6s$ET0 < 400)] <- "Cold"
# getAIref <- function(i) {
# AIrefi = subset(cmip6s, lat == cmip6s$lat[i] & lon == cmip6s$lon[i] & period =="1970_2000") %>% pull(AI.mean)
# return(AIrefi)
# }
#
# AI.ref <- sapply(as.numeric(rownames(cmip6s)), FUN = getAIref)
# saveRDS(AI.ref, "AI.ref.rds")
cmip6s$AI.ref <- readRDS("AI.ref.rds")
# getcatAIref <- function(i) {
# catAIrefi = subset(cmip6s, lat == cmip6s$lat[i] & lon == cmip6s$lon[i] & period =="1970_2000") %>% pull(cat.AI)
# return(catAIrefi)
# }
#
# catAIref.vect <- sapply(as.numeric(rownames(cmip6s)), FUN = getcatAIref)
# saveRDS(catAIref, "cat.AI.ref.rds")
cmip6s$catAIref <- readRDS("catAIref.vect.rds")
# getcatmajref <- function(i) {
# catmajrefi = subset(cmip6s, lat == cmip6s$lat[i] & lon == cmip6s$lon[i] & period =="1970_2000") %>% pull(cat.maj)
# return(catmajrefi)
# }
#
# cmip6.catmajref <- sapply(as.numeric(rownames(cmip6s)), FUN = getcatmajref)
# saveRDS(cmip6.catmajref, "cmip6.catmajref.rds")
cmip6s$catmajref <- readRDS("cmip6.catmajref.rds")
cmip6s$anomalies <- with(cmip6s, AI.mean - AI.ref)
cmip6s$same.catAI <- with(cmip6s, ifelse(cat.maj == cat.AI, "same", "diff"))
write.table(cmip6s, "cmip6s2.txt")cmip6s <- read.table("cmip6s2.txt")
# attributes a change from the studied period compared to the reference period 1970-2000. Read as: grid cell x is humid in 2030-2060 and Arid in 1970-2000, so it is WETTER
#cmip6s$change <- with(cmip6s, paste(catAIref, cat.AI, collapse = " ", sep = " to "))
cmip6s$dryer <- with(cmip6s, ifelse(catAIref == "Arid" & cat.AI %in% c("Cold","Humid","Dry subhumid","Semi-arid"), "wetter",
ifelse(catAIref == "Arid" & cat.AI == "Hyperarid", "dryer",
ifelse(catAIref == "Semi-arid" & cat.AI %in% c("Cold","Humid","Dry subhumid"), "wetter",
ifelse(catAIref == "Semi-arid" & cat.AI %in% c("Hyperarid","Arid"), "dryer",
ifelse(catAIref == "Dry subhumid" & cat.AI %in% c("Cold","Humid"), "wetter",
ifelse(catAIref == "Dry subhumid" & cat.AI %in% c("Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(catAIref == "Humid" & cat.AI == "Cold","wetter",
ifelse(catAIref == "Humid" & cat.AI %in% c("Dry subhumid","Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(catAIref == "Cold" & cat.AI %in% c("Humid","Dry subhumid","Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(catAIref == "Hyperarid" & cat.AI %in% c("Cold","Humid","Dry subhumid","Semi-arid","Arid"), "wetter",
NA)))))))))))
cmip6s$catmajref <- revalue(cmip6s$catmajref, c("hyperarid" = "Hyperarid", "arid" = "Arid", "semiarid" = "Semi-arid", "drysubhumid" = "Dry subhumid", "humid" = "Humid", "cold" = "Cold"))
cmip6s$cat.maj <- revalue(cmip6s$cat.maj, c("hyperarid" = "Hyperarid", "arid" = "Arid", "semiarid" = "Semi-arid", "drysubhumid" = "Dry subhumid", "humid" = "Humid", "cold" = "Cold"))
cmip6s$dryer.maj <- with(cmip6s, ifelse(catmajref == "Arid" & cat.maj %in% c("Cold","Humid","Dry subhumid","Semi-arid"), "wetter",
ifelse(catmajref == "Arid" & cat.maj == "Hyperarid", "dryer",
ifelse(catmajref == "Semi-arid" & cat.maj %in% c("Cold","Humid","Dry subhumid"), "wetter",
ifelse(catmajref == "Semi-arid" & cat.maj %in% c("Hyperarid","Arid"), "dryer",
ifelse(catmajref == "Dry subhumid" & cat.maj %in% c("Cold","Humid"), "wetter",
ifelse(catmajref == "Dry subhumid" & cat.maj %in% c("Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(catmajref == "Humid" & cat.maj == "Cold","wetter",
ifelse(catmajref == "Humid" & cat.maj %in% c("Dry subhumid","Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(catmajref == "Cold" & cat.maj %in% c("Humid","Dry subhumid","Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(catmajref == "Hyperarid" & cat.maj %in% c("Cold","Humid","Dry subhumid","Semi-arid","Arid"), "wetter",
NA)))))))))))
4.2 Compare cat mean and cat maj
cat mean = category of aridity obtained by using the multimodel mean AI cat maj = category of aridity obtained by using the category majoritarily obtained in the 13 models
4.2.1 Where are cat mean and cat maj different
cmip6s %>% group_by(model, period) %>% summarise(maj.equal.mean = table(same.catAI)[2], maj.diff.mean = table(same.catAI)[1], prop.same = round(maj.equal.mean/n()*100,1)) %>% knitr::kable(col.names = c("Model","Period","Number of gridcells in which cat.maj = cat.mean", "Number of gridcells in which cat.maj /= cat.mean", "Proportion of cells with the same category (%)")) %>% kableExtra::kable_styling(bootstrap_options = "bordered")| Model | Period | Number of gridcells in which cat.maj = cat.mean | Number of gridcells in which cat.maj /= cat.mean | Proportion of cells with the same category (%) |
|---|---|---|---|---|
| SSP245 | 2030_2060 | 19884 | 1798 | 89.3 |
| SSP245 | 2070_2100 | 19890 | 1792 | 89.3 |
| SSP370 | 2030_2060 | 19988 | 1694 | 89.8 |
| SSP370 | 2070_2100 | 19911 | 1771 | 89.4 |
| SSP585 | 2030_2060 | 19799 | 1883 | 88.9 |
| SSP585 | 2070_2100 | 19796 | 1886 | 88.9 |
| historical | 1850_1880 | 19907 | 1775 | 89.4 |
| historical | 1970_2000 | 19880 | 1802 | 89.3 |
| historical | 1985_2015 | 19936 | 1746 | 89.6 |
cmip6s$cat.AI <- factor(cmip6s$cat.AI, levels = c("Hyperarid","Arid","Semi-arid","Dry subhumid", "Humid","Cold"))
cmip6s$cat.maj <- factor(cmip6s$cat.maj, levels = c("Hyperarid","Arid","Semi-arid","Dry subhumid", "Humid","Cold"))
ggplot(subset(cmip6s, cat.AI != "Cold"))+
geom_col(aes(x= period, y = cat.AI, fill = cat.AI), just = -0.2, width = 0.3)+
geom_col(aes(x = period, y = cat.maj, fill = cat.maj), just = 1.2, width = 0.3)+
scale_fill_manual(values = col.cat)+
labs(fill = "", y ="")+
facet_grid(rows = vars(model), switch = "y")+
theme_minimal()+theme(axis.text.y = element_blank())
tab <- rbind(table(cmip6s$cat.AI)/length(cmip6s$cat.AI)*100, table(cmip6s$cat.maj)/length(cmip6s$cat.maj)*100) %>% t() %>% as.data.frame() %>% setNames(c("Cat mean", "Cat maj"))
knitr::kable(tab, digits =2, caption = "Proportion of gridcells in each aridity category") %>% kable_styling(bootstrap_options = "bordered")| Cat mean | Cat maj | |
|---|---|---|
| Hyperarid | 3.51 | 4.18 |
| Arid | 6.25 | 7.43 |
| Semi-arid | 7.63 | 9.19 |
| Dry subhumid | 3.39 | 0.84 |
| Humid | 28.75 | 28.53 |
| Cold | 47.90 | 47.23 |
map_list <- list()
for(i in unique(cmip6s$model)){
df <- subset(cmip6s, model == i)
for(j in unique(df$period)){
index <- paste(i, j, sep = "")
g <- ggplot()+geom_raster(data = subset(df, period == j & same.catAI == "diff"), aes(x = lon, y = lat, fill = cat.AI))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, j , sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}
}
col1 <- ggarrange(plotlist = map_list, nrow = 9, ncol = 1, common.legend = T, legend = "bottom") %>% annotate_figure(top = "cat.mean for gridcells in which\ncat.maj and cat.mean are different")
map_list2 <- list()
for(i in unique(cmip6s$model)){
df <- subset(cmip6s, model == i)
for(j in unique(df$period)){
index <- paste(i, j, sep = "")
g <- ggplot()+geom_raster(data = subset(df, period == j & same.catAI == "diff"), aes(x = lon, y = lat, fill = cat.maj))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, j , sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list2[[index]] <- g
}
}
col2 <- ggarrange(plotlist = map_list2, nrow = 9, ncol = 1, common.legend = T, legend = "bottom") %>% annotate_figure(top = "cat.maj for gridcells in which\ncat.maj and cat.mean are different")
ggarrange(col1,col2,ncol = 2, common.legend = T)
4.2.2 Plot cat maj and cat mean for reference period
ggpubr::ggarrange(plotlist = list(
ggplot() +
geom_raster(data = subset(cmip6s, period == "1970_2000"), aes(x=lon, y = lat, fill = cat.maj))+
geom_point(data = subset(cmip6s, period == "1970_2000" & nb.maj > 10), aes(x =lon, y = lat), shape = "'", size = 1, col = "grey20")+
scale_fill_manual(values = col.cat)+
labs(title = "Category chosen in majority by models", fill = "")+
ylim(-55,90)+
theme_void(base_size = 15)+
theme(legend.position = "none"),
ggplot() +
geom_raster(data = subset(cmip6s, period == "1970_2000"), aes(x=lon, y = lat, fill = cat.AI))+
geom_point(data = subset(cmip6s, period == "1970_2000" & nb.maj > 10), aes(x =lon, y = lat), shape = "'", size = 1, col = "grey20")+
scale_fill_manual(values = col.cat)+
labs(title = "Multimodel average categories of AI", fill = "")+
ylim(-55,90)+
theme_void(base_size = 15)+
theme(legend.position = "none")),
ncol = 2)
map_list <- list()
for(i in c("1850_1880","1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = cat.maj))+
geom_point(data = subset(cmip6s, period == i & model == "historical" & nb.maj > 10), aes(x= lon, y = lat), shape = "'", col = "black")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = i, fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[i]] <- g
}
map_list2 <- list()
for(i in c("1850_1880","1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = cat.AI))+
geom_point(data = subset(cmip6s, period == i & model == "historical" & nb.maj > 10), aes(x= lon, y = lat), shape = "'", col = "black")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = i, fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list2[[i]] <- g
}
ggarrange(
annotate_figure(ggarrange(plotlist = map_list, nrow = 3, common.legend = T, legend = "bottom"), top = "Majority category among models"),
annotate_figure(ggarrange(plotlist = map_list2, nrow = 3, common.legend = T, legend = "bottom"), top = "Multimodel average category"),
ncol = 2, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = cat.maj))+
# geom_point(data = subset(cmip6s, period == i & model == "historical" & change.cat == 1 & nb.maj > 10), aes(x= lon, y = lat), shape = "'")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(fill = "Cat AI maj", title = paste(i, j, sep = ", "))+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
map_list2 <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = cat.AI))+
# geom_point(data = subset(cmip6s, period == i & model == "historical" & change.cat == 1 & nb.maj > 10), aes(x= lon, y = lat), shape = "'")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(fill = "Cat AI mean", title = paste(i, j, sep = ", "))+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list2[[index]] <- g
}}
ggarrange(plotlist = map_list, nrow = 3, ncol = 2, common.legend = T, legend = "bottom")
ggarrange(plotlist = map_list2, nrow = 3, ncol = 2, common.legend = T, legend = "bottom")
4.3 Multimodel maps of cat mean
cmip6s$cat.AI <- factor(cmip6s$cat.AI, levels = c("Hyperarid","Arid","Semi-arid","Dry subhumid","Humid","Cold"))
map_list <- list()
for(i in c("1850_1880", "1970_2000","1985_2015")){
g <- ggplot() +
geom_tile(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = cat.AI))+
geom_point(data = subset(cmip6s,nb.maj > 10), aes(x = lon, y = lat), shape = "'", col = "grey60")+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste("Category AI",i , sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 1, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() +
geom_tile(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = cat.AI))+
geom_point(data = subset(cmip6s,nb.maj > 10), aes(x = lon, y = lat), shape = ".", col = "grey20", size = 0.1)+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste("Category AI",index, sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
5 Maps multimodel AI
quantile(cmip6s$AI.mean, probs = seq(0,1,0.1), na.rm = T)
## 0% 10% 20% 30% 40% 50%
## 2.902197e-03 1.993698e-01 5.922059e-01 1.019621e+00 1.457766e+00 2.127218e+00
## 60% 70% 80% 90% 100%
## 3.578980e+00 1.168470e+01 2.438867e+01 4.856852e+01 5.329875e+06
ai.breaks <- c(0,0.03,0.2,0.5,0.65,1,10,20,30)
colscale <- colorvec[-c(5,6)]map_list <- list()
for(i in c("1850_1880", "1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = AI.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = ai.breaks, palette = function(x) colscale,
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(title = i, fill = "Aridity index")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right")
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 1, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = AI.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = ai.breaks, palette = function(x) colscale,
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(fill = "Aridity index", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
6 AI changes over time and scenarios
v<-NULL
v2<-NULL
for (N in 5:30) {
v<-c(v,sum(sapply(floor(2*N/3):N,function(i) choose(N,i)*(1/2)^N*2)))# proba d'avoir 2/3 de même signe à partir de N modèles, lorsque N change
v2<-c(v2,sum(sapply(floor(2*N/3):N,function(i) choose(N,i)*(1/2)^N)))# proba d'avoir 2/3 de signe >0 à partir de N modèles, lorsque N change
}
plot(5:30,v,type="l",ylab="Proba(>2/3 models...)",xlab="Nb of models")
lines(5:30,v2,col="red")
6.1 Boxplot of changes AI
ggpubr::ggarrange(plotlist = list(
ggplot(subset(cmip6s, anomalies < 1 & anomalies > -1 & model == "historical"))+
geom_boxplot(aes(x=period, y = - anomalies, col = period), outlier.size = 1)+
scale_color_manual(values = c("#1A9850","#66BD63","#A6D96A"))+
labs(x = "", y = "difference of AI between each period and\nthe reference period 1970-2000")+
facet_grid(cols = vars(model))+
theme_minimal() + theme(legend.position = "none"),
ggplot(subset(cmip6s, anomalies < 1 & anomalies > -1 & model != "historical"))+
geom_boxplot(aes(x=period, y = anomalies, col = period), outlier.size = 1)+
scale_color_manual(values = c("#FDAE61","#F46D43"))+
scale_y_continuous(position = "right")+
labs(y = "", x = "")+
facet_grid(cols = vars(model))+
theme_minimal() + theme(legend.position = "none")),
widths = c(1.5,3),
ncol = 2, nrow = 1
)
6.2 Strictly wetter or dryer (reference period: 1970-2000)
map_list <- list()
for(i in c("1850_1880","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical" & anomalies > 0), aes(x=lon, y = lat, fill = "wetter"))+
geom_raster(data = subset(cmip6s, period == i & model == "historical" & anomalies < 0), aes(x=lon, y = lat, fill = "dryer"))+
borders(colour = "grey60")+
scale_fill_manual(values = c("#F46D43", "#74ADD1"), na.translate = F)+
labs(title = paste("AI changes between",i , "and 1970-2000", sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2)
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i & anomalies > 0), aes(x=lon, y = lat, fill = "wetter"))+
geom_raster(data = subset(cmip6s, period == j & model == i & anomalies < 0), aes(x=lon, y = lat, fill = "dryer"))+
borders(colour = "grey60")+
scale_fill_manual(values = c("#F46D43", "#74ADD1"), na.translate = F)+
labs(fill = "Compared to 1970-2000", title = i)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
6.3 Wetter or dryer, binned (reference period: 1970-2000)
ano.quantiles <- cmip6s %>% group_by(model, period) %>% summarise(q10 = quantile(anomalies, probs = 0.1, na.rm =T),
q25 = quantile(anomalies, probs = 0.25, na.rm =T),
q50 = quantile(anomalies, probs = 0.5, na.rm =T),
q75 = quantile(anomalies, probs = 0.75, na.rm =T),
q90 = quantile(anomalies, probs = 0.9, na.rm =T))
ano.quantiles <- cmip6s %>% summarise(q10 = quantile(anomalies, probs = 0.1, na.rm =T),
q25 = quantile(anomalies, probs = 0.25, na.rm =T),
q50 = quantile(anomalies, probs = 0.5, na.rm =T),
q75 = quantile(anomalies, probs = 0.75, na.rm =T),
q90 = quantile(anomalies, probs = 0.9, na.rm =T))
qbreaks <- c(-5, -1,-0.1, -0.01, 0, 0.01, 0.1, 1,5)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "#c6dbef", "#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#67001f")“Anomalies” is the multimodel AI average minus the reference AI (period 1970-2000). More blue: AI mean is superior to AI ref, hence wetter than the ref. More red: the anomalie is negative, hence AI mean is inferior to AI ref, hence dryer.
AS OF NOW THE SAME QUANTILES ARE USED FOR ALL MAPS.
map_list <- list()
for(i in c("1850_1880","1985_2015")){
#quant = subset(ano.quantiles, model == "historical" & period == i)
#b = quant[1,c(3:7)] %>% as.numeric
b = qbreaks
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = anomalies))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = b, palette = function(x) rev(colscale),
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(title = i, fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right")
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
b = qbreaks
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = anomalies))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = b, palette = function(x) rev(colscale),
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(fill = "Compared to 1970-2000", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
6.4 Wetter or dryer, binned, % (reference period: 1970-2000)
cmip6s$anomalies.percent <- with(cmip6s, (AI.mean-AI.ref)/AI.ref*100)
pbreaks <- c(-40, -30,-20, -10, 0, 10, 20, 30, 40)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "#c6dbef", "#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#67001f")“Anomalies” is the multimodel AI average minus the reference AI (period 1970-2000). More blue: AI mean is superior to AI ref, hence wetter than the ref. More red: the anomalie is negative, hence AI mean is inferior to AI ref, hence dryer.
map_list <- list()
for(i in c("1850_1880","1985_2015")){
#quant = subset(ano.quantiles, model == "historical" & period == i)
#b = quant[1,c(3:7)] %>% as.numeric
b = pbreaks
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = anomalies.percent))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = b, palette = function(x) rev(colscale),
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(title = i, fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right")
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 1, nrow = 2, common.legend = T, legend = "bottom") %>% annotate_figure(top = "% change of AI index compared to 1970-2000")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
b = pbreaks
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = anomalies.percent))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = b, palette = function(x) rev(colscale),
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(fill = "% change of AI index\ncompared to 1970-2000", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
7 Difference between future and reference category of AI, using cat maj
7.1 Strictly dryer or wetter category cat maj
cmip6s$change.catmaj <- with(cmip6s, ifelse(cat.maj == catmajref, 0, 1))
map_list <- list()
for(i in c("1850_1880","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical" & change.catmaj == 1), aes(x=lon, y = lat, fill = dryer.maj))+
borders(colour = "grey60")+
scale_fill_manual(values = c("#F46D43", "#74ADD1"), na.translate = F)+
labs(title = paste("AI changes between", i , "and 1970-2000", sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2, common.legend = T)
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = dryer.maj))+
geom_raster(data = subset(cmip6s, period == j & model == "historical" & change.catmaj == 1 & nb.maj > 10), aes(x= lon, y = lat, fill = "'"))+
borders(colour = "grey60")+
scale_fill_manual(values = c("#F46D43", "#74ADD1"), na.translate = F)+
labs(fill = "Compared to 1970-2000", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
7.2 Detailed differences cat maj
7.2.1 Detailed changes and colors cat maj
cmip6s$change.catmaj <- with(cmip6s, ifelse(cat.maj == catmajref, 0, 1))
cmip6s$change.cat.maj <- with(cmip6s, ifelse(catmajref == "Arid" & cat.maj %in% c("Cold","Humid"),"Arid to cold or humid",
ifelse(catmajref == "Arid" & cat.maj %in% c("Semi-Arid","Dry subhumid"), "Arid to semi-arid or dry subhumid",
ifelse(catmajref == "Arid" & cat.maj == "Hyperarid", "Arid to hyperarid",
ifelse(catmajref == "Semi-arid" & cat.maj == "Dry subhumid", "Semi-arid to dry subhumid",
ifelse(catmajref == "Semi-arid" & cat.maj == "Arid", "Semi-arid to arid",
ifelse(catmajref == "Semi-arid" & cat.maj == "Hyperarid", "Semi-arid to hyperarid",
ifelse(catmajref == "Semi-arid" & cat.maj %in% c("Humid","Cold"), "Semi-arid to humid or cold",
ifelse(catmajref == "Dry subhumid" & cat.maj %in% c("Semi-arid","Arid","Hyperarid"), "Dry subhumid to arid, semi-arid or hyperarid",
ifelse(catmajref == "Dry subhumid" & cat.maj %in% c("Humid","Cold"), "Dry subhumid to humid or cold",
ifelse(catmajref == "Humid" & cat.maj == "Cold","Humid to cold",
ifelse(catmajref == "Humid" & cat.maj %in% c("Dry subhumid", "Semi-arid"), "Humid to dry-subhumid or semi-arid",
ifelse(catmajref == "Humid" & cat.maj %in% c("Arid", "Hyperarid"), "Humid to arid or hyperarid",
ifelse(catmajref == "Cold" & cat.maj == "Humid", "Cold to humid",
ifelse(catmajref == "Cold" & cat.maj %in% c("Dry subhumid","Semi-arid","Arid","Hyperarid"), "Cold to dryland",
ifelse(catmajref == "Hyperarid" & cat.maj == "Arid", "Hyperarid to arid",
ifelse(catmajref == "Hyperarid" & cat.maj %in% c("Semi-arid","Dry-subhumid"),"Hyperarid to semi arid or dry subhumid",
ifelse(catmajref == "Hyperarid" & cat.maj %in% c("Humid", "Cold"), "Hyperarid to humid or cold", NA
))))))))))))))))))
cmip6s$change.cat.maj <- factor(cmip6s$change.cat.maj,
levels = c("Arid to hyperarid","Semi-arid to arid", "Dry subhumid to arid, semi-arid or hyperarid", "Humid to dry-subhumid or semi-arid", "Cold to humid","Hyperarid to arid", "Arid to semi-arid or dry subhumid", "Dry subhumid to humid or cold", "Semi-arid to dry subhumid", "Semi-arid to humid or cold"))
table(cmip6s$change.cat.maj)
##
## Arid to hyperarid
## 491
## Semi-arid to arid
## 1462
## Dry subhumid to arid, semi-arid or hyperarid
## 649
## Humid to dry-subhumid or semi-arid
## 3088
## Cold to humid
## 3700
## Hyperarid to arid
## 301
## Arid to semi-arid or dry subhumid
## 4
## Dry subhumid to humid or cold
## 201
## Semi-arid to dry subhumid
## 236
## Semi-arid to humid or cold
## 381
colors_changes <- c("Hyperarid to arid" = "#E7D7B6",
"Arid to semi-arid or dry subhumid" = colorvec[7],
"Dry subhumid to humid or cold" = colorvec[8],
"Semi-arid to dry subhumid" = colorvec[9],
"Semi-arid to humid or cold" = colorvec[10],
"Humid to cold" = colorvec[11],
"Cold to humid" = "#E7D7B6",
"Humid to dry-subhumid or semi-arid" = colorvec[5],
"Dry subhumid to arid, semi-arid or hyperarid" = colorvec[4],
"Semi-arid to arid" = colorvec[3],
"Arid to hyperarid" = colorvec[1])7.2.2 Changes towards dryer categories cat maj
7.2.2.1 Historical
map_list <- list()
for(i in c("1850_1880","1985_2015")){
g <- ggplot() +
geom_raster(data = subset(cmip6s, period == i & model == "historical" & change.catmaj == 1 & dryer.maj == "dryer"),
aes(x=lon, y = lat, fill = change.cat.maj))+
borders(colour = "grey60")+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(title = i, fill = "Change of category\ncompared to\n1970-2000")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom")
7.2.2.1.1 Future
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i & change.catmaj == 1 & dryer.maj == "dryer"),
aes(x=lon, y = lat, fill = change.cat.maj))+
# geom_raster(data = subset(cmip6s, period == i & model == "historical" & change.catmaj == 1 & nb.maj > 10), aes(x= lon, y = lat, fill = "'"))+
borders(colour = "grey60")+
scale_fill_manual(values = colors_changes, na.translate = F)+
# scale_fill_viridis_d()+
labs(fill = "Change of category\ncompared to\n1970-2000", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[index]] <- g
}}
ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
7.2.3 Changes towards wetter categories cat maj
7.2.3.1 Historical
map_list <- list()
for(i in c("1850_1880","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical" & change.catmaj == 1 & dryer.maj == "wetter"),
aes(x=lon, y = lat, fill = change.cat.maj))+
borders(colour = "grey60", ylim = c(-40,40))+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(title = i, fill = "Change of category\ncompared to\n1970-2000")+
theme_void()+
ylim(-50,50)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom")
7.2.3.2 Future
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i & change.catmaj == 1 & dryer.maj == "wetter"),
aes(x=lon, y = lat, fill = change.cat.maj))+
borders(colour = "grey60", ylim = c(-40,40))+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(fill = "Change of category\ncompared to\n1970-2000", title = index)+
theme_void()+ylim(-50,50)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list, nrow = 3, ncol = 2, common.legend = T, legend = "bottom")
8 Difference between future and reference category of AI, using cat mean
8.1 Stricly dryer or wetter category cat mean
cmip6s$change.catmean <- with(cmip6s, ifelse(cat.maj == catmajref, 0, 1))
map_list <- list()
for(i in c("1850_1880","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical" & change.catmean == 1), aes(x=lon, y = lat, fill = dryer))+
borders(colour = "grey60")+
scale_fill_manual(values = c("#F46D43", "#74ADD1"), na.translate = F)+
labs(title = paste("AI changes between",i , "and 1970-2000", sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i & change.catmean == 1), aes(x=lon, y = lat, fill = dryer))+
borders(colour = "grey60")+
scale_fill_manual(values = c("#F46D43", "#74ADD1"), na.translate = F)+
labs(fill = "Compared to 1970-2000", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
8.2 Detailed differences cat mean
8.2.1 Detailed changes and colors
cmip6s$change.catmean <- with(cmip6s, ifelse(cat.AI == catAIref, 0, 1))
cmip6s$change.cat.mean <- with(cmip6s, ifelse(catAIref == "Arid" & cat.AI %in% c("Cold","Humid"),"Arid to cold or humid",
ifelse(catAIref == "Arid" & cat.AI %in% c("Semi-Arid","Dry subhumid"), "Arid to semi-arid or dry subhumid",
ifelse(catAIref == "Arid" & cat.AI == "Hyperarid", "Arid to hyperarid",
ifelse(catAIref == "Semi-arid" & cat.AI == "Dry subhumid", "Semi-arid to dry subhumid",
ifelse(catAIref == "Semi-arid" & cat.AI == "Arid", "Semi-arid to arid",
ifelse(catAIref == "Semi-arid" & cat.AI == "Hyperarid", "Semi-arid to hyperarid",
ifelse(catAIref == "Semi-arid" & cat.AI %in% c("Humid","Cold"), "Semi-arid to humid or cold",
ifelse(catAIref == "Dry subhumid" & cat.AI %in% c("Semi-arid","Arid","Hyperarid"), "Dry subhumid to arid, semi-arid or hyperarid",
ifelse(catAIref == "Dry subhumid" & cat.AI %in% c("Humid","Cold"), "Dry subhumid to humid or cold",
ifelse(catAIref == "Humid" & cat.AI == "Cold","Humid to cold",
ifelse(catAIref == "Humid" & cat.AI %in% c("Dry subhumid","Semi-arid"), "Humid to dry-subhumid or semi-arid",
ifelse(catAIref == "Humid" & cat.AI %in% c("Arid", "Hyperarid"), "Humid to arid or hyperarid",
ifelse(catAIref == "Cold" & cat.AI == "Humid", "Cold to humid",
ifelse(catAIref == "Cold" & cat.AI %in% c("Dry subhumid","Semi-arid","Arid","Hyperarid"), "Cold to dryland",
ifelse(catAIref == "Hyperarid" & cat.AI == "Arid", "Hyperarid to arid",
ifelse(catAIref == "Hyperarid" & cat.AI %in% c("Semi-arid","Dry-subhumid"),"Hyperarid to semi arid or dry subhumid",
ifelse(catAIref == "Hyperarid" & cat.AI %in% c("Humid", "Cold"), "Hyperarid to humid or cold", NA
))))))))))))))))))
table(cmip6s$change.cat.mean)
##
## Arid to hyperarid
## 638
## Cold to humid
## 4370
## Dry subhumid to arid, semi-arid or hyperarid
## 2214
## Dry subhumid to humid or cold
## 297
## Humid to cold
## 148
## Humid to dry-subhumid or semi-arid
## 2624
## Hyperarid to arid
## 248
## Semi-arid to arid
## 1196
## Semi-arid to dry subhumid
## 276
## Semi-arid to humid or cold
## 1
cmip6s$change.cat.mean <- factor(cmip6s$change.cat.mean,
levels = c("Arid to hyperarid","Semi-arid to arid", "Dry subhumid to arid, semi-arid or hyperarid", "Humid to dry-subhumid or semi-arid", "Cold to humid","Hyperarid to arid", "Arid to semi-arid or dry subhumid", "Dry subhumid to humid or cold", "Semi-arid to dry subhumid", "Semi-arid to humid or cold"))
table(cmip6s$change.cat.mean)
##
## Arid to hyperarid
## 638
## Semi-arid to arid
## 1196
## Dry subhumid to arid, semi-arid or hyperarid
## 2214
## Humid to dry-subhumid or semi-arid
## 2624
## Cold to humid
## 4370
## Hyperarid to arid
## 248
## Arid to semi-arid or dry subhumid
## 0
## Dry subhumid to humid or cold
## 297
## Semi-arid to dry subhumid
## 276
## Semi-arid to humid or cold
## 1
colors_changes <- c("Hyperarid to arid" = "#E7D7B6",
"Arid to semi-arid or dry subhumid" = colorvec[7],
"Dry subhumid to humid or cold" = colorvec[8],
"Semi-arid to dry subhumid" = colorvec[9],
"Semi-arid to humid or cold" = colorvec[10],
"Humid to cold" = colorvec[11],
"Cold to humid" = "#E7D7B6",
"Humid to dry-subhumid or semi-arid" = colorvec[5],
"Dry subhumid to arid, semi-arid or hyperarid" = colorvec[4],
"Semi-arid to arid" = colorvec[3],
"Arid to hyperarid" = colorvec[1])8.2.2 Changes towards dryer categories cat mean
8.2.2.1 Historical
map_list <- list()
for(i in c("1850_1880","1985_2015")){
g <- ggplot() +
geom_raster(data = subset(cmip6s, period == i & model == "historical" & change.catmean == 1 & dryer == "dryer"),
aes(x=lon, y = lat, fill = change.cat.mean))+
borders(colour = "grey60")+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(title = i, fill = "Change of category\ncompared to\n1970-2000")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom")
8.2.2.2 Future
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i & change.catmean == 1 & dryer == "dryer"),
aes(x=lon, y = lat, fill = change.cat.mean))+
borders(colour = "grey60")+
scale_fill_manual(values = colors_changes)+
labs(fill = "Change of category\ncompared to\n1970-2000", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[index]] <- g
}}
ggarrange(plotlist = map_list, ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
8.2.3 Changes towards wetter categories cat mean
8.2.3.1 Historical
map_list <- list()
for(i in c("1850_1880","1985_2015")){
g <- ggplot() + geom_tile(data = subset(cmip6s, period == i & model == "historical" & change.catmaj == 1 & dryer == "wetter"),
aes(x=lon, y = lat, fill = change.cat.mean))+
borders(colour = "grey60", ylim = c(-40,40))+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(title = i, fill = "Change of category\ncompared to\n1970-2000")+
theme_void()+ylim(-50,50)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom")
8.2.3.2 Future
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i & change.catmean == 1 & dryer == "wetter"),
aes(x=lon, y = lat, fill = change.cat.mean))+
borders(colour = "grey60", ylim = c(-40,40))+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(fill = "Change of category\ncompared to\n1970-2000", title = index)+
theme_void()+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
9 Tables
9.1 Changes of aridity category using cat mean
tab <- cmip6s %>% group_by(model, period, Continent) %>% summarise(Cold = table(cat.AI)[1], Hyperarid = table(cat.AI)[2], Arid = table(cat.AI)[3], Semiarid = table(cat.AI)[4], Drysubhumid = table(cat.AI)[5], Humid = table(cat.AI)[6], total = n())
write.table(tab, "tab.catAI.txt")
tab.future <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(period, model, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(period, model) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(period + model ~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
kable(tab.future[order(tab.future$model, decreasing = F),]) %>% kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(7,10), italic = T, include_thead = T) %>% row_spec(2, bold = T) | model | period | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 3.4 | 6.0 | 7.1 | 3.3 | 19.8 | 28.8 | 48.9 | 2.6 |
| 2 | historical | 1970_2000 | 3.3 | 6.0 | 7.0 | 3.3 | 19.6 | 27.8 | 49.9 | 2.6 |
| 3 | historical | 1985_2015 | 3.3 | 6.1 | 6.9 | 3.3 | 19.6 | 27.3 | 50.6 | 2.6 |
| 4 | SSP245 | 2030_2060 | 3.5 | 6.3 | 7.8 | 3.3 | 20.9 | 28.8 | 47.7 | 2.6 |
| 7 | SSP245 | 2070_2100 | 3.6 | 6.4 | 7.8 | 3.5 | 21.3 | 29.4 | 46.6 | 2.6 |
| 5 | SSP370 | 2030_2060 | 3.2 | 6.7 | 7.6 | 3.0 | 20.5 | 28.8 | 48.1 | 2.6 |
| 8 | SSP370 | 2070_2100 | 4.0 | 6.3 | 8.3 | 3.7 | 22.3 | 28.5 | 46.6 | 2.6 |
| 6 | SSP585 | 2030_2060 | 3.6 | 6.2 | 7.9 | 3.5 | 21.2 | 29.3 | 47.0 | 2.6 |
| 9 | SSP585 | 2070_2100 | 3.8 | 6.4 | 8.4 | 3.6 | 22.2 | 30.1 | 45.1 | 2.6 |
# ggplot(tab.future, aes(x = period))+
# geom_point(aes(y = Hyperarid, col = "Hyperarid", shape = model))+
# geom_line(aes(y=Hyperarid, col = "Hyperarid", group = model, lty = model))+
# geom_point(aes(y = Arid, col = "Arid", shape = model))+
# geom_line(aes(y=Arid, col = "Arid", group = model, lty = model))+
# geom_point(aes(y = get('Semi-arid'), col = "Semi-arid", shape = model))+
# geom_line(aes(y=get('Semi-arid'), col = "Semi-arid", group = model, lty = model))+
# geom_point(aes(y = get('Dry subhumid'), col = "Dry subhumid", shape = model))+
# geom_line(aes(y=get('Dry subhumid'), col = "Dry subhumid", group = model, lty = model))+
# geom_point(aes(y = Humid, col = "Humid", shape = model))+
# geom_line(aes(y= Humid, col = "Humid", group = model, lty = model))+
# scale_color_manual(values = col.cat)+
# theme_minimal()
cmip6$cat.AI <- factor(cmip6$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
tab.percent <- cmip6 %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","POLAR","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(period, model, cat.AI, source) %>%
summarise(count = n()) %>%
ungroup() %>%
group_by(period, model, cat.AI) %>%
summarise(mmmean = mean(count, na.rm = T), mmsd = sd(count, na.rm = T)) %>%
mutate(percent = round(mmmean/sum(mmmean)*100, 1), sd.percent = round(mmsd/sum(mmmean)*100,1))
write.table(tab.percent, "tab.percent.txt")tab.percent <- read.table("tab.percent.txt")
df245 <- rbind(subset(tab.percent, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.percent, model == "SSP245")) %>%
group_by(period, model) %>%
mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
df245$cat.AI <- factor(df245$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
b245 <- ggplot(data = df245, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = paste(percent, " ± ", sd.percent, sep = "")), size = 3)+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 2-4.5", y = "%", x = "", col = "", fill = "")+
theme_minimal()
df370 <- rbind(subset(tab.percent, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.percent, model == "SSP370")) %>%
group_by(period, model) %>%
mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
df370$cat.AI <- factor(df370$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
b370 <- ggplot(data = df370, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = paste(percent, " ± ", sd.percent, sep = "")), size = 3)+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 3-7.0", y = "%", x = "", col = "", fill = "")+
theme_minimal()
df585 <- rbind(subset(tab.percent, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.percent, model == "SSP585")) %>%
group_by(period, model) %>%
mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
df585$cat.AI <- factor(df585$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
b585 <- ggplot(data = df585, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = paste(percent, " ± ", sd.percent, sep = "")), size = 3)+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 5-8.5", y = "%", x = "", col = "", fill = "")+
theme_minimal()
ggarrange(plotlist = list(b245, b370, b585), common.legend = T, legend = "bottom", ncol = 3)
9.2 Changes of aridity category using cat maj
tab.maj <- cmip6s %>% group_by(model, period, Continent) %>% summarise(Cold = table(cat.maj)[1], Hyperarid = table(cat.maj)[2], Arid = table(cat.maj)[3], Semiarid = table(cat.maj)[4], Drysubhumid = table(cat.maj)[5], Humid = table(cat.maj)[6], total = n())
write.table(tab.maj, "tab.catmaj.txt")
tab.future.maj <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(period, model, cat.maj) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(period, model) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(period + model ~cat.maj) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
kable(tab.future.maj[order(tab.future.maj$model, decreasing = F),]) %>% kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(7,10), italic = T, include_thead = T) %>% row_spec(2, bold = T) | model | period | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 4.2 | 7.1 | 8.7 | 0.8 | 20.8 | 28.3 | 48.3 | 2.6 |
| 2 | historical | 1970_2000 | 4.1 | 7.1 | 8.6 | 0.7 | 20.5 | 28.1 | 48.9 | 2.6 |
| 3 | historical | 1985_2015 | 4.0 | 7.0 | 8.4 | 0.9 | 20.3 | 27.3 | 49.9 | 2.6 |
| 4 | SSP245 | 2030_2060 | 4.0 | 7.8 | 9.3 | 0.8 | 21.9 | 28.4 | 47.2 | 2.6 |
| 7 | SSP245 | 2070_2100 | 4.2 | 7.7 | 9.4 | 0.9 | 22.2 | 29.1 | 46.1 | 2.6 |
| 5 | SSP370 | 2030_2060 | 4.1 | 7.2 | 9.4 | 0.8 | 21.5 | 28.6 | 47.3 | 2.6 |
| 8 | SSP370 | 2070_2100 | 4.5 | 7.7 | 10.0 | 0.9 | 23.1 | 28.4 | 45.9 | 2.6 |
| 6 | SSP585 | 2030_2060 | 4.2 | 7.5 | 9.6 | 1.0 | 22.3 | 28.8 | 46.3 | 2.6 |
| 9 | SSP585 | 2070_2100 | 4.5 | 7.9 | 9.4 | 0.9 | 22.7 | 30.0 | 44.6 | 2.6 |
tab.flow.catmaj <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","POLAR","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(period, model, cat.maj) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(period, model) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL)
df245 <- rbind(subset(tab.flow.catmaj, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.flow.catmaj, model == "SSP245")) %>%
mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.maj %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b245 <- ggplot(data = df245, aes(x = period, y = percent))+
geom_col(aes(group = cat.maj, col = cat.maj, fill = cat.maj))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 2-4.5", y = "%", x = "")+
theme_minimal()
df370 <- rbind(subset(tab.flow.catmaj, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.flow.catmaj, model == "SSP370")) %>% mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.maj %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b370 <- ggplot(data = df370, aes(x = period, y = percent))+
geom_col(aes(group = cat.maj, col = cat.maj, fill = cat.maj))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 3-7.0", y = "%", x = "")+
theme_minimal()
df585 <- rbind(subset(tab.flow.catmaj, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.flow.catmaj, model == "SSP585")) %>% mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.maj %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b585 <- ggplot(data = df585, aes(x = period, y = percent))+
geom_col(aes(group = cat.maj, col = cat.maj, fill = cat.maj))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 5-8.5", y = "%", x = "")+
theme_minimal()
ggarrange(plotlist = list(b245, b370, b585), common.legend = T, legend = "bottom", ncol = 3)
9.3 Changes by continent, cat mean
cmip6$cat.AI <- factor(cmip6$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
tab.percent.cont <- cmip6 %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","POLAR","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(period, model, Continent, cat.AI, source) %>%
summarise(count = n()) %>%
ungroup() %>%
group_by(period, model, Continent, cat.AI) %>%
summarise(mmmean = mean(count, na.rm = T), mmsd = sd(count, na.rm = T)) %>%
mutate(percent = round(mmmean/sum(mmmean)*100, 1), sd.percent = round(mmsd/sum(mmmean)*100,1))
write.table(tab.percent.cont, "tab.percent.continent.txt")tab.percent.cont <- read.table("tab.percent.continent.txt")
tab_list_percent <- list()
tab_list_sd <- list()
for(i in unique(tab.percent.cont$Continent)){
tab.i <- subset(tab.percent.cont, Continent == i)
df.percent <- tab.i %>% reshape2::dcast(period + model ~ cat.AI, value.var = "percent")
df.sd <- tab.i %>% reshape2::dcast(period + model ~ cat.AI, value.var = "sd.percent")
# sd for a sum: square root of sum of squared sd
drycats <- which(names(df.percent) %in% c("Hyperarid","Arid","Semi-arid","Dry subhumid"))
df.percent <- df.percent %>% mutate("Sum drylands" = rowSums(.[drycats], na.rm = T))
df.sd <- df.sd %>% mutate("Sum drylands" = round(sqrt(rowSums((.[drycats])^2, na.rm = T)),2))
missing <- setdiff(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"), names(df.percent))
df.percent[, missing] <- 0
df.sd[,missing] <- 0
df.percent <- df.percent %>% select(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
df.percent[is.na(df.percent)] <- 0
tab_list_percent[[i]] <- df.percent
df.sd <- df.sd %>% select(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
df.sd[is.na(df.sd)] <- 0
tab_list_sd[[i]] <- df.sd
}
k_list <- list()
kdiff_list <- list()
for(i in names(tab_list_percent)){
df <- tab_list_percent[[i]] %>% select(c("model", "period"))
prop.ref <- tab_list_percent[[i]] %>% filter(period == "1970_2000")
prop.ref <- prop.ref %>% rbind(prop.ref[rep(1,8),])
df2 <- tab_list_percent[[i]][,c(3:10)] - prop.ref[,c(3:10)] %>% round(1)
diff.prop.percent <- cbind(df, df2)
sd.ref <- tab_list_sd[[i]] %>% filter(period == "1970_2000")
sd.ref <- sd.ref %>% rbind(sd.ref[rep(1,8),])
df3 <- sqrt(tab_list_sd[[i]][,c(3:10)]^2 + sd.ref[,c(3:10)]^2) %>% round(1)
diff.sd.percent <- cbind(df, df3)
df$Hyperarid <- paste(tab_list_percent[[i]]$Hyperarid, tab_list_sd[[i]]$Hyperarid, sep = " ± ")
df$Arid <- paste(tab_list_percent[[i]]$Arid, tab_list_sd[[i]]$Arid, sep = " ± ")
df$'Semi-Arid' <- paste(tab_list_percent[[i]][,5], tab_list_sd[[i]][,5], sep = " ± ")
df$'Dry subhumid' <- paste(tab_list_percent[[i]][,6], tab_list_sd[[i]][,6], sep = " ± ")
df$'Sum drylands' <- paste(tab_list_percent[[i]][,7], tab_list_sd[[i]][,7], sep = " ± ")
df$Humid <- paste(tab_list_percent[[i]]$Humid, tab_list_sd[[i]]$Humid, sep = " ± ")
df$Cold <- paste(tab_list_percent[[i]]$Cold, tab_list_sd[[i]]$Cold, sep = " ± ")
df.diff <- df
df.diff$Hyperarid <- paste(round(diff.prop.percent$Hyperarid, 1), round(diff.sd.percent$Hyperarid, 1), sep = " ± ")
df.diff$Arid <- paste(round(diff.prop.percent$Arid, 1), round(diff.sd.percent$Arid, 1), sep = " ± ")
df.diff$'Semi-Arid' <- paste(round(diff.prop.percent[,5], 1), round(diff.sd.percent[,5], 1), sep = " ± ")
df.diff$'Dry subhumid' <- paste(round(diff.prop.percent[,6], 1), round(diff.sd.percent[,6], 1), sep = " ± ")
df.diff$'Sum drylands' <- paste(round(diff.prop.percent[,7],1), round(diff.sd.percent[,7], 1), sep = " ± ")
df.diff$Humid <- paste(round(diff.prop.percent$Humid, 1), round(diff.sd.percent$Humid, 1), sep = " ± ")
df.diff$Cold <- paste(round(diff.prop.percent$Cold, 1), round(diff.sd.percent$Cold, 1), sep = " ± ")
k_list[[i]] <- kable(df[order(df$model, decreasing = F),], caption = i) %>% kable_styling(bootstrap_options = "bordered") %>%
column_spec(8, italic = T, background = colorvec[5]) %>% row_spec(2, bold = T)
kdiff_list[[i]] <- kable(df.diff[order(df.diff$model, decreasing = F),], caption = i) %>% kable_styling(bootstrap_options = "bordered") %>%
column_spec(8, italic = T, background = colorvec[5]) %>% row_spec(2, bold = T)
}9.3.1 Proportion of aridity categories by continent
k_list| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 27.2 ± 4.7 | 17.1 ± 4.9 | 15.6 ± 3.6 | 6.3 ± 1.4 | 66.2 ± 7.81 | 33.7 ± 7.1 | 0 ± 0 |
| 2 | historical | 1970_2000 | 26.9 ± 4.7 | 16.7 ± 4.2 | 15.1 ± 3.4 | 6.2 ± 1.5 | 64.9 ± 7.32 | 32.8 ± 6.2 | 2.3 ± 0 |
| 3 | historical | 1985_2015 | 26.5 ± 4.8 | 17.1 ± 4.6 | 15.4 ± 3.3 | 6 ± 1.5 | 65 ± 7.57 | 32.7 ± 6.3 | 2.2 ± 0 |
| 4 | SSP245 | 2030_2060 | 27 ± 4.8 | 18 ± 4.9 | 16.5 ± 3.5 | 6.4 ± 1.7 | 67.9 ± 7.89 | 32 ± 7.6 | 0 ± 0 |
| 7 | SSP245 | 2070_2100 | 27.9 ± 4.7 | 17.4 ± 5 | 17.2 ± 3.4 | 6.2 ± 1.5 | 68.7 ± 7.8 | 31.3 ± 7.9 | 0 ± 0 |
| 5 | SSP370 | 2030_2060 | 26 ± 5 | 16.6 ± 3.6 | 17.2 ± 1.8 | 6.8 ± 1 | 66.6 ± 6.5 | 32.3 ± 6.7 | 1.1 ± 0 |
| 8 | SSP370 | 2070_2100 | 28 ± 3.5 | 17.6 ± 4.8 | 16.6 ± 3.1 | 6.2 ± 1.6 | 68.4 ± 6.89 | 30.4 ± 6.4 | 1.2 ± 0 |
| 6 | SSP585 | 2030_2060 | 27.1 ± 4.6 | 17.6 ± 4.8 | 16.8 ± 3.5 | 6.3 ± 1.5 | 67.8 ± 7.66 | 32.1 ± 7.5 | 0 ± 0 |
| 9 | SSP585 | 2070_2100 | 29.7 ± 10.5 | 17.3 ± 5.3 | 16.2 ± 4.2 | 6 ± 1.5 | 69.2 ± 12.58 | 30.9 ± 8.6 | 0 ± 0 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 3.9 ± 2.3 | 12.2 ± 1.9 | 9.2 ± 1.5 | 4 ± 1.4 | 29.3 ± 3.62 | 32.4 ± 7.7 | 38 ± 9.2 |
| 2 | historical | 1970_2000 | 3.8 ± 2.1 | 12 ± 1.8 | 8.9 ± 1.5 | 3.8 ± 1.1 | 28.5 ± 3.33 | 30.7 ± 8.5 | 40.5 ± 9.6 |
| 3 | historical | 1985_2015 | 3.7 ± 2.2 | 11.8 ± 1.6 | 8.6 ± 1.5 | 3.7 ± 1.1 | 27.8 ± 3.3 | 30.1 ± 7.6 | 41.8 ± 8.8 |
| 4 | SSP245 | 2030_2060 | 3.5 ± 1.8 | 12.5 ± 1.6 | 9.4 ± 1.6 | 4.3 ± 1.7 | 29.7 ± 3.35 | 34.3 ± 8.1 | 35.8 ± 9.2 |
| 7 | SSP245 | 2070_2100 | 3.7 ± 2 | 12.5 ± 1.7 | 9.5 ± 1.7 | 4.2 ± 1.7 | 29.9 ± 3.56 | 36.2 ± 8.6 | 33.7 ± 9.3 |
| 5 | SSP370 | 2030_2060 | 3.4 ± 2.3 | 12.4 ± 1.5 | 8.9 ± 1.7 | 3.7 ± 1.3 | 28.4 ± 3.48 | 34 ± 7.2 | 37.3 ± 9.5 |
| 8 | SSP370 | 2070_2100 | 4.6 ± 1.9 | 12.5 ± 1.5 | 9.6 ± 1.5 | 3.8 ± 1.2 | 30.5 ± 3.09 | 35.2 ± 9.2 | 34.1 ± 9.7 |
| 6 | SSP585 | 2030_2060 | 3.8 ± 2 | 12.5 ± 1.6 | 9.5 ± 1.8 | 4.2 ± 1.6 | 30 ± 3.52 | 35.4 ± 8.4 | 34.4 ± 9.3 |
| 9 | SSP585 | 2070_2100 | 4.8 ± 3.2 | 13.1 ± 3.2 | 10.4 ± 3.4 | 4.2 ± 1.5 | 32.5 ± 5.86 | 36.5 ± 11.7 | 30.7 ± 9.9 |
CENTRAL-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 8.4 ± 3.6 | 30.1 ± 5.1 | 16.1 ± 8.7 | 54.6 ± 10.71 | 45 ± 6.1 | 0 ± 0 |
| 2 | historical | 1970_2000 | 0 ± 0 | 8.4 ± 4.1 | 29.7 ± 7.6 | 14.7 ± 7.8 | 52.8 ± 11.64 | 44.9 ± 12 | 2 ± 0 |
| 3 | historical | 1985_2015 | 0 ± 0 | 9 ± 4.2 | 30.7 ± 7 | 14.8 ± 7.4 | 54.5 ± 11.02 | 43.8 ± 11.5 | 1.4 ± 0 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 11.9 ± 6.2 | 36.3 ± 8 | 13.9 ± 6.2 | 62.1 ± 11.87 | 37.6 ± 8.6 | 0 ± 0 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 12.3 ± 5.5 | 38.1 ± 8.5 | 14.4 ± 7.9 | 64.8 ± 12.84 | 34.8 ± 8.6 | 0 ± 0 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 12.6 ± 6.3 | 31.4 ± 7.1 | 15 ± 7.9 | 59 ± 12.35 | 40.7 ± 11.8 | 0 ± 0 |
| 8 | SSP370 | 2070_2100 | 4 ± 5.2 | 16 ± 5.5 | 35.5 ± 9.5 | 13.2 ± 5.2 | 68.7 ± 13.21 | 30.9 ± 10.6 | 0 ± 0 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 13 ± 7 | 37.6 ± 9 | 14.1 ± 7.1 | 64.7 ± 13.43 | 35 ± 9.4 | 0 ± 0 |
| 9 | SSP585 | 2070_2100 | 2 ± 2.4 | 16.6 ± 8 | 41.2 ± 7.7 | 13.3 ± 7.7 | 73.1 ± 13.72 | 26.6 ± 8.8 | 0 ± 0 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 1.9 ± 1.2 | 9.6 ± 4.1 | 4.5 ± 2.8 | 16 ± 5.11 | 49 ± 15.3 | 34.8 ± 20.2 |
| 2 | historical | 1970_2000 | 0 ± 0 | 1.5 ± 1 | 8.9 ± 3.4 | 3.9 ± 2.1 | 14.3 ± 4.12 | 44.9 ± 16.1 | 40.6 ± 20.4 |
| 3 | historical | 1985_2015 | 0 ± 0 | 1.3 ± 1 | 8.4 ± 2.9 | 3.8 ± 2 | 13.5 ± 3.66 | 46 ± 16.1 | 40.3 ± 19.9 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 1.9 ± 0.9 | 10.3 ± 3.6 | 5.1 ± 1.9 | 17.3 ± 4.17 | 52.7 ± 15.4 | 29.8 ± 19.7 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 2.2 ± 1.1 | 10.1 ± 4.8 | 5.1 ± 2.3 | 17.4 ± 5.44 | 55.4 ± 14.4 | 27 ± 18.6 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 2.1 ± 1 | 9.7 ± 4.7 | 4.8 ± 2.5 | 16.6 ± 5.42 | 52.7 ± 15.7 | 30.4 ± 20 |
| 8 | SSP370 | 2070_2100 | 0 ± 0 | 3.1 ± 1.1 | 11.4 ± 5.9 | 6.4 ± 3.8 | 20.9 ± 7.1 | 52.8 ± 14.3 | 26 ± 18.6 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 2.3 ± 1.1 | 10.5 ± 3.7 | 4.9 ± 2.4 | 17.7 ± 4.55 | 55.2 ± 15.8 | 26.9 ± 19.9 |
| 9 | SSP585 | 2070_2100 | 0 ± 0 | 3 ± 1.7 | 11.1 ± 5.4 | 6.1 ± 2.9 | 20.2 ± 6.36 | 57.2 ± 12.8 | 22.5 ± 17.3 |
EUROPE-AFRICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 5.9 ± 4.9 | 23.6 ± 7 | 21.3 ± 8.8 | 8.8 ± 3.3 | 59.6 ± 12.7 | 24.9 ± 14.2 | 14.5 ± 0 |
| 2 | historical | 1970_2000 | 7 ± 5.5 | 26.5 ± 7.5 | 23.3 ± 10.3 | 9.9 ± 3.2 | 66.7 ± 14.24 | 27.5 ± 16.5 | 4.5 ± 7.5 |
| 3 | historical | 1985_2015 | 7.1 ± 5.9 | 26.3 ± 7.1 | 22.2 ± 7.7 | 10.3 ± 4.2 | 65.9 ± 12.73 | 27.8 ± 14.3 | 5.2 ± 7 |
| 4 | SSP245 | 2030_2060 | 9.9 ± 6.6 | 28.9 ± 8.6 | 26.7 ± 8.9 | 9.4 ± 3.4 | 74.9 ± 14.43 | 23.1 ± 16.2 | 0.9 ± 0 |
| 7 | SSP245 | 2070_2100 | 11.7 ± 5.7 | 28.6 ± 8.8 | 27.2 ± 8.1 | 9.5 ± 3.1 | 77 ± 13.61 | 21.9 ± 13.9 | 0 ± 0 |
| 5 | SSP370 | 2030_2060 | 10.3 ± 6.3 | 28.2 ± 8.4 | 26 ± 8.7 | 9 ± 2.8 | 73.5 ± 13.92 | 24.8 ± 14 | 0.5 ± 0 |
| 8 | SSP370 | 2070_2100 | 13.2 ± 6.2 | 27.3 ± 8.6 | 28.3 ± 9.7 | 8.7 ± 3.6 | 77.5 ± 14.81 | 21.2 ± 16.2 | 0 ± 0 |
| 6 | SSP585 | 2030_2060 | 11.1 ± 6.4 | 28.8 ± 8.3 | 27.5 ± 8.8 | 8.7 ± 3 | 76.1 ± 14.01 | 22.6 ± 14.1 | 0.2 ± 0 |
| 9 | SSP585 | 2070_2100 | 15.2 ± 5.7 | 29.1 ± 10.1 | 27.9 ± 8.8 | 8 ± 2.8 | 80.2 ± 14.82 | 18.6 ± 13.6 | 0 ± 0 |
NORTH-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 0.4 ± 0.2 | 5.8 ± 5.6 | 4.3 ± 2.6 | 10.5 ± 6.18 | 35.1 ± 9 | 54.2 ± 10.9 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0.4 ± 0.2 | 4.3 ± 4.4 | 4.2 ± 2.4 | 8.9 ± 5.02 | 34.5 ± 9.8 | 56.5 ± 11.7 |
| 3 | historical | 1985_2015 | 0 ± 0 | 0.3 ± 0.2 | 4.6 ± 3.8 | 4.1 ± 2.2 | 9 ± 4.4 | 33.6 ± 10.3 | 57.2 ± 11.5 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 0.4 ± 0.3 | 6.5 ± 5.5 | 4.7 ± 2.6 | 11.6 ± 6.09 | 37.5 ± 8.7 | 50.8 ± 11.1 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 0.5 ± 0.3 | 6.4 ± 5.1 | 5.1 ± 2.9 | 12 ± 5.87 | 39.7 ± 9 | 48.1 ± 11 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 0.5 ± 0.4 | 5.9 ± 4.8 | 4.6 ± 2.7 | 11 ± 5.52 | 37 ± 9.7 | 51.8 ± 12.2 |
| 8 | SSP370 | 2070_2100 | 0.1 ± 0 | 0.8 ± 0.7 | 7.6 ± 4 | 5.2 ± 2 | 13.7 ± 4.53 | 38.4 ± 10.6 | 47.7 ± 11.2 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 0.5 ± 0.4 | 6.6 ± 5.2 | 5.2 ± 2.8 | 12.3 ± 5.92 | 38.2 ± 9.1 | 49.3 ± 11.7 |
| 9 | SSP585 | 2070_2100 | 0.2 ± 0 | 1 ± 1.4 | 7 ± 6.1 | 5.2 ± 2.5 | 13.4 ± 6.74 | 42.1 ± 9.3 | 44.5 ± 11.3 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 5.3 ± 0 | 32 ± 26 | 36.7 ± 17.1 | 6.6 ± 3.1 | 80.6 ± 31.27 | 18.9 ± 12.5 | 0.3 ± 0.2 |
| 2 | historical | 1970_2000 | 5.2 ± 0 | 31.3 ± 26.4 | 36.8 ± 17.4 | 6.8 ± 3.1 | 80.1 ± 31.77 | 19.3 ± 12.8 | 0.5 ± 0.4 |
| 3 | historical | 1985_2015 | 5.2 ± 0 | 33.4 ± 26.1 | 35.5 ± 17.9 | 6.8 ± 3.2 | 80.9 ± 31.81 | 18.4 ± 12.3 | 0.6 ± 0.3 |
| 4 | SSP245 | 2030_2060 | 10.4 ± 0 | 35.8 ± 23.5 | 31.7 ± 15.1 | 5.7 ± 2.6 | 83.6 ± 28.05 | 15.9 ± 11.9 | 0.3 ± 0.3 |
| 7 | SSP245 | 2070_2100 | 10.5 ± 0 | 37.9 ± 22.2 | 29.1 ± 13.1 | 6.2 ± 3.3 | 83.7 ± 25.99 | 16.2 ± 12 | 0.1 ± 0 |
| 5 | SSP370 | 2030_2060 | 10.7 ± 0 | 34.8 ± 22.6 | 33.4 ± 15.3 | 5.8 ± 2.5 | 84.7 ± 27.41 | 14.9 ± 8.1 | 0.3 ± 0.2 |
| 8 | SSP370 | 2070_2100 | 10.5 ± 10.2 | 38.1 ± 19.7 | 28.5 ± 13.8 | 5.7 ± 3.2 | 82.8 ± 26.32 | 16.8 ± 16.3 | 0.3 ± 0.3 |
| 6 | SSP585 | 2030_2060 | 5.6 ± 3.6 | 35.6 ± 25.3 | 33.8 ± 16.7 | 6.7 ± 3.6 | 81.7 ± 30.74 | 17.7 ± 13.8 | 0.3 ± 0.3 |
| 9 | SSP585 | 2070_2100 | 9.3 ± 6.4 | 39.4 ± 21.7 | 29.4 ± 12.9 | 5.5 ± 2.5 | 83.6 ± 26.16 | 16.2 ± 12.5 | 0 ± 0 |
SOUTH-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0.1 ± 0.1 | 2.1 ± 1.7 | 15.2 ± 8.7 | 7.4 ± 2.6 | 24.8 ± 9.24 | 73.2 ± 11.7 | 1.9 ± 1 |
| 2 | historical | 1970_2000 | 0.1 ± 0.1 | 1.8 ± 1.3 | 13.9 ± 4.3 | 7.3 ± 2.3 | 23.1 ± 5.05 | 74.5 ± 7.3 | 2.3 ± 2.6 |
| 3 | historical | 1985_2015 | 0.2 ± 0.1 | 2 ± 1.2 | 14.5 ± 5.1 | 7.5 ± 2.4 | 24.2 ± 5.76 | 73.6 ± 7.7 | 2.1 ± 2.6 |
| 4 | SSP245 | 2030_2060 | 0.2 ± 0.2 | 2.7 ± 2.2 | 17.4 ± 9.6 | 8.8 ± 3.6 | 29.1 ± 10.49 | 69.4 ± 13.1 | 1.3 ± 0.9 |
| 7 | SSP245 | 2070_2100 | 0.2 ± 0.1 | 3.3 ± 3.5 | 18.3 ± 9.1 | 9.3 ± 4.6 | 31.1 ± 10.78 | 67.5 ± 14.7 | 1.3 ± 0.7 |
| 5 | SSP370 | 2030_2060 | 0.2 ± 0.1 | 2.2 ± 1.7 | 16.3 ± 5.5 | 8.8 ± 3.7 | 27.5 ± 6.84 | 70.5 ± 8.9 | 1.9 ± 1.6 |
| 8 | SSP370 | 2070_2100 | 0.2 ± 0.1 | 3.3 ± 2.5 | 18.6 ± 7 | 8.7 ± 3.8 | 30.8 ± 8.35 | 67.3 ± 10.8 | 1.8 ± 1.8 |
| 6 | SSP585 | 2030_2060 | 0.2 ± 0.1 | 3.1 ± 2.5 | 18.6 ± 9 | 9.2 ± 4.4 | 31.1 ± 10.33 | 67.6 ± 13.1 | 1.2 ± 0.8 |
| 9 | SSP585 | 2070_2100 | 0.6 ± 1 | 4.4 ± 3.8 | 19.6 ± 9.5 | 9.7 ± 3.9 | 34.3 ± 11 | 64.4 ± 12.6 | 1.1 ± 0.7 |
9.3.2 Changes in proportion of aridity categories by continent
kdiff_list| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0.3 ± 6.6 | 0.4 ± 6.5 | 0.5 ± 5 | 0.1 ± 2.1 | 1.3 ± 10.7 | 0.9 ± 9.4 | -2.3 ± 0 |
| 2 | historical | 1970_2000 | 0 ± 6.6 | 0 ± 5.9 | 0 ± 4.8 | 0 ± 2.1 | 0 ± 10.4 | 0 ± 8.8 | 0 ± 0 |
| 3 | historical | 1985_2015 | -0.4 ± 6.7 | 0.4 ± 6.2 | 0.3 ± 4.7 | -0.2 ± 2.1 | 0.1 ± 10.5 | -0.1 ± 8.8 | -0.1 ± 0 |
| 4 | SSP245 | 2030_2060 | 0.1 ± 6.7 | 1.3 ± 6.5 | 1.4 ± 4.9 | 0.2 ± 2.3 | 3 ± 10.8 | -0.8 ± 9.8 | -2.3 ± 0 |
| 7 | SSP245 | 2070_2100 | 1 ± 6.6 | 0.7 ± 6.5 | 2.1 ± 4.8 | 0 ± 2.1 | 3.8 ± 10.7 | -1.5 ± 10 | -2.3 ± 0 |
| 5 | SSP370 | 2030_2060 | -0.9 ± 6.9 | -0.1 ± 5.5 | 2.1 ± 3.8 | 0.6 ± 1.8 | 1.7 ± 9.8 | -0.5 ± 9.1 | -1.2 ± 0 |
| 8 | SSP370 | 2070_2100 | 1.1 ± 5.9 | 0.9 ± 6.4 | 1.5 ± 4.6 | 0 ± 2.2 | 3.5 ± 10.1 | -2.4 ± 8.9 | -1.1 ± 0 |
| 6 | SSP585 | 2030_2060 | 0.2 ± 6.6 | 0.9 ± 6.4 | 1.7 ± 4.9 | 0.1 ± 2.1 | 2.9 ± 10.6 | -0.7 ± 9.7 | -2.3 ± 0 |
| 9 | SSP585 | 2070_2100 | 2.8 ± 11.5 | 0.6 ± 6.8 | 1.1 ± 5.4 | -0.2 ± 2.1 | 4.3 ± 14.6 | -1.9 ± 10.6 | -2.3 ± 0 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0.1 ± 3.1 | 0.2 ± 2.6 | 0.3 ± 2.1 | 0.2 ± 1.8 | 0.8 ± 4.9 | 1.7 ± 11.5 | -2.5 ± 13.3 |
| 2 | historical | 1970_2000 | 0 ± 3 | 0 ± 2.5 | 0 ± 2.1 | 0 ± 1.6 | 0 ± 4.7 | 0 ± 12 | 0 ± 13.6 |
| 3 | historical | 1985_2015 | -0.1 ± 3 | -0.2 ± 2.4 | -0.3 ± 2.1 | -0.1 ± 1.6 | -0.7 ± 4.7 | -0.6 ± 11.4 | 1.3 ± 13 |
| 4 | SSP245 | 2030_2060 | -0.3 ± 2.8 | 0.5 ± 2.4 | 0.5 ± 2.2 | 0.5 ± 2 | 1.2 ± 4.7 | 3.6 ± 11.7 | -4.7 ± 13.3 |
| 7 | SSP245 | 2070_2100 | -0.1 ± 2.9 | 0.5 ± 2.5 | 0.6 ± 2.3 | 0.4 ± 2 | 1.4 ± 4.9 | 5.5 ± 12.1 | -6.8 ± 13.4 |
| 5 | SSP370 | 2030_2060 | -0.4 ± 3.1 | 0.4 ± 2.3 | 0 ± 2.3 | -0.1 ± 1.7 | -0.1 ± 4.8 | 3.3 ± 11.1 | -3.2 ± 13.5 |
| 8 | SSP370 | 2070_2100 | 0.8 ± 2.8 | 0.5 ± 2.3 | 0.7 ± 2.1 | 0 ± 1.6 | 2 ± 4.5 | 4.5 ± 12.5 | -6.4 ± 13.6 |
| 6 | SSP585 | 2030_2060 | 0 ± 2.9 | 0.5 ± 2.4 | 0.6 ± 2.3 | 0.4 ± 1.9 | 1.5 ± 4.8 | 4.7 ± 12 | -6.1 ± 13.4 |
| 9 | SSP585 | 2070_2100 | 1 ± 3.8 | 1.1 ± 3.7 | 1.5 ± 3.7 | 0.4 ± 1.9 | 4 ± 6.7 | 5.8 ± 14.5 | -9.8 ± 13.8 |
CENTRAL-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 0 ± 5.5 | 0.4 ± 9.2 | 1.4 ± 11.7 | 1.8 ± 15.8 | 0.1 ± 13.5 | -2 ± 0 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 5.8 | 0 ± 10.7 | 0 ± 11 | 0 ± 16.5 | 0 ± 17 | 0 ± 0 |
| 3 | historical | 1985_2015 | 0 ± 0 | 0.6 ± 5.9 | 1 ± 10.3 | 0.1 ± 10.8 | 1.7 ± 16 | -1.1 ± 16.6 | -0.6 ± 0 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 3.5 ± 7.4 | 6.6 ± 11 | -0.8 ± 10 | 9.3 ± 16.6 | -7.3 ± 14.8 | -2 ± 0 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 3.9 ± 6.9 | 8.4 ± 11.4 | -0.3 ± 11.1 | 12 ± 17.3 | -10.1 ± 14.8 | -2 ± 0 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 4.2 ± 7.5 | 1.7 ± 10.4 | 0.3 ± 11.1 | 6.2 ± 17 | -4.2 ± 16.8 | -2 ± 0 |
| 8 | SSP370 | 2070_2100 | 4 ± 5.2 | 7.6 ± 6.9 | 5.8 ± 12.2 | -1.5 ± 9.4 | 15.9 ± 17.6 | -14 ± 16 | -2 ± 0 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 4.6 ± 8.1 | 7.9 ± 11.8 | -0.6 ± 10.5 | 11.9 ± 17.8 | -9.9 ± 15.2 | -2 ± 0 |
| 9 | SSP585 | 2070_2100 | 2 ± 2.4 | 8.2 ± 9 | 11.5 ± 10.8 | -1.4 ± 11 | 20.3 ± 18 | -18.3 ± 14.9 | -2 ± 0 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 0.4 ± 1.6 | 0.7 ± 5.3 | 0.6 ± 3.5 | 1.7 ± 6.6 | 4.1 ± 22.2 | -5.8 ± 28.7 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 1.4 | 0 ± 4.8 | 0 ± 3 | 0 ± 5.8 | 0 ± 22.8 | 0 ± 28.8 |
| 3 | historical | 1985_2015 | 0 ± 0 | -0.2 ± 1.4 | -0.5 ± 4.5 | -0.1 ± 2.9 | -0.8 ± 5.5 | 1.1 ± 22.8 | -0.3 ± 28.5 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 0.4 ± 1.3 | 1.4 ± 5 | 1.2 ± 2.8 | 3 ± 5.9 | 7.8 ± 22.3 | -10.8 ± 28.4 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 0.7 ± 1.5 | 1.2 ± 5.9 | 1.2 ± 3.1 | 3.1 ± 6.8 | 10.5 ± 21.6 | -13.6 ± 27.6 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 0.6 ± 1.4 | 0.8 ± 5.8 | 0.9 ± 3.3 | 2.3 ± 6.8 | 7.8 ± 22.5 | -10.2 ± 28.6 |
| 8 | SSP370 | 2070_2100 | 0 ± 0 | 1.6 ± 1.5 | 2.5 ± 6.8 | 2.5 ± 4.3 | 6.6 ± 8.2 | 7.9 ± 21.5 | -14.6 ± 27.6 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 0.8 ± 1.5 | 1.6 ± 5 | 1 ± 3.2 | 3.4 ± 6.1 | 10.3 ± 22.6 | -13.7 ± 28.5 |
| 9 | SSP585 | 2070_2100 | 0 ± 0 | 1.5 ± 2 | 2.2 ± 6.4 | 2.2 ± 3.6 | 5.9 ± 7.6 | 12.3 ± 20.6 | -18.1 ± 26.7 |
EUROPE-AFRICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | -1.1 ± 7.4 | -2.9 ± 10.3 | -2 ± 13.5 | -1.1 ± 4.6 | -7.1 ± 19.1 | -2.6 ± 21.8 | 10 ± 7.5 |
| 2 | historical | 1970_2000 | 0 ± 7.8 | 0 ± 10.6 | 0 ± 14.6 | 0 ± 4.5 | 0 ± 20.1 | 0 ± 23.3 | 0 ± 10.6 |
| 3 | historical | 1985_2015 | 0.1 ± 8.1 | -0.2 ± 10.3 | -1.1 ± 12.9 | 0.4 ± 5.3 | -0.8 ± 19.1 | 0.3 ± 21.8 | 0.7 ± 10.3 |
| 4 | SSP245 | 2030_2060 | 2.9 ± 8.6 | 2.4 ± 11.4 | 3.4 ± 13.6 | -0.5 ± 4.7 | 8.2 ± 20.3 | -4.4 ± 23.1 | -3.6 ± 7.5 |
| 7 | SSP245 | 2070_2100 | 4.7 ± 7.9 | 2.1 ± 11.6 | 3.9 ± 13.1 | -0.4 ± 4.5 | 10.3 ± 19.7 | -5.6 ± 21.6 | -4.5 ± 7.5 |
| 5 | SSP370 | 2030_2060 | 3.3 ± 8.4 | 1.7 ± 11.3 | 2.7 ± 13.5 | -0.9 ± 4.3 | 6.8 ± 19.9 | -2.7 ± 21.6 | -4 ± 7.5 |
| 8 | SSP370 | 2070_2100 | 6.2 ± 8.3 | 0.8 ± 11.4 | 5 ± 14.1 | -1.2 ± 4.8 | 10.8 ± 20.5 | -6.3 ± 23.1 | -4.5 ± 7.5 |
| 6 | SSP585 | 2030_2060 | 4.1 ± 8.4 | 2.3 ± 11.2 | 4.2 ± 13.5 | -1.2 ± 4.4 | 9.4 ± 20 | -4.9 ± 21.7 | -4.3 ± 7.5 |
| 9 | SSP585 | 2070_2100 | 8.2 ± 7.9 | 2.6 ± 12.6 | 4.6 ± 13.5 | -1.9 ± 4.3 | 13.5 ± 20.6 | -8.9 ± 21.4 | -4.5 ± 7.5 |
NORTH-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 0 ± 0.3 | 1.5 ± 7.1 | 0.1 ± 3.5 | 1.6 ± 8 | 0.6 ± 13.3 | -2.3 ± 16 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 0.3 | 0 ± 6.2 | 0 ± 3.4 | 0 ± 7.1 | 0 ± 13.9 | 0 ± 16.5 |
| 3 | historical | 1985_2015 | 0 ± 0 | -0.1 ± 0.3 | 0.3 ± 5.8 | -0.1 ± 3.3 | 0.1 ± 6.7 | -0.9 ± 14.2 | 0.7 ± 16.4 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 0 ± 0.4 | 2.2 ± 7 | 0.5 ± 3.5 | 2.7 ± 7.9 | 3 ± 13.1 | -5.7 ± 16.1 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 0.1 ± 0.4 | 2.1 ± 6.7 | 0.9 ± 3.8 | 3.1 ± 7.7 | 5.2 ± 13.3 | -8.4 ± 16.1 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 0.1 ± 0.4 | 1.6 ± 6.5 | 0.4 ± 3.6 | 2.1 ± 7.5 | 2.5 ± 13.8 | -4.7 ± 16.9 |
| 8 | SSP370 | 2070_2100 | 0.1 ± 0 | 0.4 ± 0.7 | 3.3 ± 5.9 | 1 ± 3.1 | 4.8 ± 6.8 | 3.9 ± 14.4 | -8.8 ± 16.2 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 0.1 ± 0.4 | 2.3 ± 6.8 | 1 ± 3.7 | 3.4 ± 7.8 | 3.7 ± 13.4 | -7.2 ± 16.5 |
| 9 | SSP585 | 2070_2100 | 0.2 ± 0 | 0.6 ± 1.4 | 2.7 ± 7.5 | 1 ± 3.5 | 4.5 ± 8.4 | 7.6 ± 13.5 | -12 ± 16.3 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0.1 ± 0 | 0.7 ± 37.1 | -0.1 ± 24.4 | -0.2 ± 4.4 | 0.5 ± 44.6 | -0.4 ± 17.9 | -0.2 ± 0.4 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 37.3 | 0 ± 24.6 | 0 ± 4.4 | 0 ± 44.9 | 0 ± 18.1 | 0 ± 0.6 |
| 3 | historical | 1985_2015 | 0 ± 0 | 2.1 ± 37.1 | -1.3 ± 25 | 0 ± 4.5 | 0.8 ± 45 | -0.9 ± 17.8 | 0.1 ± 0.5 |
| 4 | SSP245 | 2030_2060 | 5.2 ± 0 | 4.5 ± 35.3 | -5.1 ± 23 | -1.1 ± 4 | 3.5 ± 42.4 | -3.4 ± 17.5 | -0.2 ± 0.5 |
| 7 | SSP245 | 2070_2100 | 5.3 ± 0 | 6.6 ± 34.5 | -7.7 ± 21.8 | -0.6 ± 4.5 | 3.6 ± 41 | -3.1 ± 17.5 | -0.4 ± 0.4 |
| 5 | SSP370 | 2030_2060 | 5.5 ± 0 | 3.5 ± 34.8 | -3.4 ± 23.2 | -1 ± 4 | 4.6 ± 42 | -4.4 ± 15.1 | -0.2 ± 0.4 |
| 8 | SSP370 | 2070_2100 | 5.3 ± 10.2 | 6.8 ± 32.9 | -8.3 ± 22.2 | -1.1 ± 4.5 | 2.7 ± 41.3 | -2.5 ± 20.7 | -0.2 ± 0.5 |
| 6 | SSP585 | 2030_2060 | 0.4 ± 3.6 | 4.3 ± 36.6 | -3 ± 24.1 | -0.1 ± 4.8 | 1.6 ± 44.2 | -1.6 ± 18.8 | -0.2 ± 0.5 |
| 9 | SSP585 | 2070_2100 | 4.1 ± 6.4 | 8.1 ± 34.2 | -7.4 ± 21.7 | -1.3 ± 4 | 3.5 ± 41.2 | -3.1 ± 17.9 | -0.5 ± 0.4 |
SOUTH-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0.1 | 0.3 ± 2.1 | 1.3 ± 9.7 | 0.1 ± 3.5 | 1.7 ± 10.5 | -1.3 ± 13.8 | -0.4 ± 2.8 |
| 2 | historical | 1970_2000 | 0 ± 0.1 | 0 ± 1.8 | 0 ± 6.1 | 0 ± 3.3 | 0 ± 7.1 | 0 ± 10.3 | 0 ± 3.7 |
| 3 | historical | 1985_2015 | 0.1 ± 0.1 | 0.2 ± 1.8 | 0.6 ± 6.7 | 0.2 ± 3.3 | 1.1 ± 7.7 | -0.9 ± 10.6 | -0.2 ± 3.7 |
| 4 | SSP245 | 2030_2060 | 0.1 ± 0.2 | 0.9 ± 2.6 | 3.5 ± 10.5 | 1.5 ± 4.3 | 6 ± 11.6 | -5.1 ± 15 | -1 ± 2.8 |
| 7 | SSP245 | 2070_2100 | 0.1 ± 0.1 | 1.5 ± 3.7 | 4.4 ± 10.1 | 2 ± 5.1 | 8 ± 11.9 | -7 ± 16.4 | -1 ± 2.7 |
| 5 | SSP370 | 2030_2060 | 0.1 ± 0.1 | 0.4 ± 2.1 | 2.4 ± 7 | 1.5 ± 4.4 | 4.4 ± 8.5 | -4 ± 11.5 | -0.4 ± 3.1 |
| 8 | SSP370 | 2070_2100 | 0.1 ± 0.1 | 1.5 ± 2.8 | 4.7 ± 8.2 | 1.4 ± 4.4 | 7.7 ± 9.8 | -7.2 ± 13 | -0.5 ± 3.2 |
| 6 | SSP585 | 2030_2060 | 0.1 ± 0.1 | 1.3 ± 2.8 | 4.7 ± 10 | 1.9 ± 5 | 8 ± 11.5 | -6.9 ± 15 | -1.1 ± 2.7 |
| 9 | SSP585 | 2070_2100 | 0.5 ± 1 | 2.6 ± 4 | 5.7 ± 10.4 | 2.4 ± 4.5 | 11.2 ± 12.1 | -10.1 ± 14.6 | -1.2 ± 2.7 |
9.3.3 Changes in proportion of aridity categories, figure
list_plots <- list()
for(i in names(tab_list_percent)){
df.percent <- tab_list_percent[[i]]
df.sd <- tab_list_sd[[i]]
df <- merge(df.percent, df.sd, by = c("model", "period"))
g <- ggplot(data = df)+geom_point(aes(x=period,y= get("Sum drylands.x"), col = model), shape = "\u2605", size = 5, position = position_dodge(width = 0.5))+
geom_errorbar(aes(x = period, ymin = get("Sum drylands.x")-get("Sum drylands.y"), ymax = get("Sum drylands.x")+get("Sum drylands.y"), col = model), position = position_dodge(width = 0.5))+
scale_color_manual(values = colorvec[c(1,3,9,11)])+
ylim(0,100)+
labs(x="", y = "Percent of drylands", title = i)+
theme_minimal()
list_plots[[i]] <- g
}
ggarrange(plotlist = list_plots, ncol = 2, nrow = 4, common.legend = T, legend = "bottom")
tab.flow <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","POLAR","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(period, model, Continent, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(period, model, Continent) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL)
df245 <- rbind(subset(tab.flow, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.flow, model == "SSP245")) %>% mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b245 <- ggplot(data = df245, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
facet_grid(rows = vars(Continent), switch = "y")+
scale_y_continuous(position = "right")+
labs(title = "SSP 2-4.5", y = "%", x = "")+
theme_minimal()
df370 <- rbind(subset(tab.flow, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.flow, model == "SSP370")) %>% mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b370 <- ggplot(data = df370, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
facet_grid(rows = vars(Continent), switch = "y")+
scale_y_continuous(position = "right")+ labs(title = "SSP 3-7.0", y = "%", x = "")+
theme_minimal()
df585 <- rbind(subset(tab.flow, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.flow, model == "SSP585")) %>% mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b585 <- ggplot(data = df585, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
facet_grid(rows = vars(Continent), switch = "y")+
scale_y_continuous(position = "right")+ labs(title = "SSP 5-8.5", y = "%", x = "")+
theme_minimal()
ggarrange(plotlist = list(b245, b370, b585), common.legend = T, legend = "bottom", ncol = 3)
10 Changes of variables over time and scenarios
10.1 Temperature
quantile(cmip6s$t.mean, probs = seq(0,1,0.1))
## 0% 10% 20% 30% 40% 50%
## -53.4587313 -42.5828261 -28.4644864 -17.7502317 -7.2080554 -0.2580962
## 60% 70% 80% 90% 100%
## 7.1198406 15.3318920 22.8072968 26.4971129 34.3727997
t.breaks <- c(-50,-40,-30,-20,-10,0,10,20,30)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "#c6dbef", "#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#67001f")map_list <- list()
for(i in c("1850_1880", "1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = t.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = t.breaks, palette = function(x) colscale,
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(title = i, fill = "Mean annual temperature, °C")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right")
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 1, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = t.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = t.breaks, palette = function(x) colscale,
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(fill = "Mean annual temperature, °C", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
#labels = c("2030-2060", "2070-2100"10.2 Precipitation
quantile(cmip6s$pr.mean, probs = seq(0,1,0.1))
## 0% 10% 20% 30% 40% 50%
## 8.579696 55.000319 104.132310 200.852004 337.042710 463.946055
## 60% 70% 80% 90% 100%
## 579.741473 712.099280 959.490392 1431.315602 6863.723388
p.breaks <- c(10,50,100,200,300,400,500,600,700)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "#c6dbef", "#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#67001f")map_list <- list()
for(i in c("1850_1880","1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = pr.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = p.breaks, palette = function(x) rev(colscale),
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(title = i, fill = "Annual mean precipitation")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right")
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 1, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = pr.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = p.breaks, palette = function(x) rev(colscale),
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(fill = "Annual mean\nprecipitation, mm", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")